A high-reliability mold automation control method and device

Abstract

The present application provides a high-reliability mold automation control method, which belongs to the field of control technology. The method detects the actual size of the target product through a detection system, determines the wear parameter value of the cutting edge based on the actual size, and adjusts the relative angle between the cutting edge and the lower die base based on the wear parameter value of the cutting edge to minimize the impact of cutting edge wear on the actual product size. This method has the technical effect of improving the reliability and automation level of the equipment without the need for personnel to make adjustments at any time. The present application provides a high-reliability mold automation control device and an electronic device.

Description

Technical Field

[0001] The present application relates to the technical field of control equipment, and in particular to a high-reliability mold automation control method. Background Art

[0002] A shearing die is a tool used in metalworking, using shearing forces to cut metal materials into the desired dimensions. These dies are typically made of high-strength steel, offering excellent wear resistance and durability. Shearing dies are widely used in industrial production, particularly in the automotive, aerospace, shipbuilding, and machinery manufacturing industries. Using shearing dies can significantly improve production efficiency, reduce material waste, and ensure the smoothness and precision of the cut surface.

[0003] During the actual shearing process of the shearing die, the edge of the die will wear out, which will cause the size of the bright band after shearing to change, resulting in a decrease in product precision. At this time, it is necessary to manually adjust the angle of the edge tool or directly replace the tool, which has a low degree of intelligence and wastes some human resources. Summary of the Invention

[0004] The embodiments of the present application provide a high-reliability mold automation control method and device to improve the above-mentioned problems.

[0005] To achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, embodiments of the present application provide a high-reliability mold automation control method applicable to a shearing mold system, the shearing mold system including a conveying system, a shearing system, a detection system, and a controller, the shearing system including a lower mold base and a cutting edge that moves relative to the lower mold base, the method comprising:

[0007] The controller controls the conveying system to deliver the objects to be sheared into the shearing system;

[0008] The controller controls the shearing system to shear the object to be sheared so that the object to be sheared forms a target product;

[0009] The controller controls the conveying system to send the object to be sheared into the detection system, which is used to detect the actual size of the target product;

[0010] The controller determines the wear parameter value of the cutting edge in the shearing system based on the actual size. The wear parameter value is used to characterize the degree of wear of the cutting edge.

[0011] The controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value.

[0012] In conjunction with the first aspect, in some embodiments, the controller determines a wear parameter value of the cutting edge in the shearing system based on the actual size, where the wear parameter value is used to characterize the degree of wear of the cutting edge, including:

[0013] The controller obtains the original thickness of the object to be sheared and, based on the detection system, obtains the actual thickness and target size of the target product. The target size is the length from the cutting position of the cutting edge to the edge of the target product, perpendicular to the cutting edge. The actual thickness is the distance from the highest point of the target product to the ideal plane when it is on the ideal plane.

[0014] The controller inputs the original thickness, actual thickness and target size into a trained neural network training model, and the neural network training model is used to output a predicted wear parameter value based on the input original thickness, actual thickness and target size;

[0015] The controller confirms the wear parameter value based on the output results of the neural network training model.

[0016] In conjunction with the first aspect, in some embodiments, the controller inputs the original thickness, actual thickness, and target size into a trained neural network training model. Before the neural network training model outputs a predicted wear parameter value based on the input original thickness, actual thickness, and target size, the controller includes:

[0017] The controller obtains a training set, the training set including multiple sets of training data, wherein a set of training data includes at least one original thickness, an actual thickness, a target size, and a wear parameter value;

[0018] The controller inputs the training set into the original neural network training model, and the original neural network training model is used to iteratively calculate an original thickness, an actual thickness, a target size, and a wear parameter value;

[0019] When the preset conditions are met, the training of the original neural network training model is stopped and the trained neural network training model is output.

[0020] In conjunction with the first aspect, in some embodiments, the controller inputs the training set into the original neural network training model, and the original neural network training model is used to iteratively calculate an original thickness, an actual thickness, a target size, and a wear parameter value, including:

[0021] The controller takes an original thickness, an actual thickness, and a target size as three different dimensions, and the three different dimensions constitute the target vector of the wear parameter value;

[0022] The controller iteratively calculates the data of different groups in the training set.

[0023] In conjunction with the first aspect, in some embodiments, when a preset condition is met, stopping the training of the original neural network training model and outputting the trained neural network training model includes:

[0024] The controller inputs a set of training data, including an original thickness, an actual thickness, and a target size, into the original neural network training model, and obtains the predicted wear parameter value output by the original neural network training model;

[0025] The controller determines the error value based on the predicted wear parameter value output by the initial neural network training model and the wear parameter value in the training data;

[0026] When the error value is less than the preset error value, the training is stopped and the trained neural network training model is output.

[0027] In combination with the first aspect, in some embodiments, the controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value, including:

[0028] When the wear parameter value is less than the preset wear parameter value, the controller does not control the relative angle between the cutting edge and the lower die base to change;

[0029] When the wear parameter value is greater than or equal to the first preset wear parameter value, the larger the wear parameter value is, the larger the relative angle between the cutting edge and the lower die seat controlled by the controller is;

[0030] When the wear parameter value is greater than or equal to the second preset wear parameter value, the controller controls the shearing die system to stop working and sends an alarm signal through the alarm system.

[0031] In conjunction with the first aspect, in some embodiments, when the wear parameter value is greater than or equal to a first preset wear parameter value, the larger the wear parameter value is, the larger the relative angle between the cutting edge and the lower die seat controlled by the controller is, satisfying:

[0032]

[0033] Where α is the relative angle change between the cutting edge and the lower die seat, K is the wear parameter value, Q is a constant, h is the original thickness, H is the hardness of the object to be sheared, and M is the yield strength of the object to be sheared.

[0034] In combination with the first aspect, in some embodiments, the controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value, including:

[0035] The controller controls the relative angle between the lower die base and the cutting edge to change based on the relative angle between the cutting edge and the lower die base.

[0036] In a second aspect, the present application proposes a high-reliability mold automation control device applicable to a shearing mold system, wherein the shearing mold system includes a conveying system, a shearing system, a detection system, and a controller. The shearing system includes a lower mold base and a cutting edge that moves relative to the lower mold base. The device is configured as follows:

[0037] The controller controls the conveying system to deliver the objects to be sheared into the shearing system;

[0038] The controller controls the shearing system to shear the object to be sheared so that the object to be sheared forms a target product;

[0039] The controller controls the conveying system to send the object to be sheared into the detection system, which is used to detect the actual size of the target product;

[0040] The controller determines the wear parameter value of the cutting edge in the shearing system based on the actual size. The wear parameter value is used to characterize the degree of wear of the cutting edge.

[0041] The controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value.

[0042] In conjunction with the second aspect, in some embodiments, the apparatus is configured to:

[0043] Based on the actual size, the controller determines the wear parameter value of the cutting edge in the shearing system. The wear parameter value is used to characterize the degree of wear of the cutting edge, including:

[0044] The controller obtains the original thickness of the object to be sheared and, based on the detection system, obtains the actual thickness and target size of the target product. The target size is the length from the cutting position of the cutting edge to the edge of the target product, perpendicular to the cutting edge. The actual thickness is the distance from the highest point of the target product to the ideal plane when it is on the ideal plane.

[0045] The controller inputs the original thickness, actual thickness and target size into a trained neural network training model, and the neural network training model is used to output a predicted wear parameter value based on the input original thickness, actual thickness and target size;

[0046] The controller confirms the wear parameter value based on the output results of the neural network training model.

[0047] In conjunction with the second aspect, in some embodiments, the apparatus is configured to:

[0048] The controller inputs the original thickness, actual thickness, and target size into the trained neural network training model. The neural network training model is used to output the predicted wear parameter value based on the input original thickness, actual thickness, and target size, including:

[0049] The controller obtains a training set, the training set including multiple sets of training data, wherein a set of training data includes at least one original thickness, an actual thickness, a target size, and a wear parameter value;

[0050] The controller inputs the training set into the original neural network training model, and the original neural network training model is used to iteratively calculate an original thickness, an actual thickness, a target size, and a wear parameter value;

[0051] When the preset conditions are met, the training of the original neural network training model is stopped and the trained neural network training model is output.

[0052] In conjunction with the second aspect, in some embodiments, the apparatus is configured to:

[0053] The controller inputs the training set into the original neural network training model, which is used to iteratively calculate an original thickness, an actual thickness, a target size, and a wear parameter value, including:

[0054] The controller takes an original thickness, an actual thickness, and a target size as three different dimensions, and the three different dimensions constitute the target vector of the wear parameter value;

[0055] The controller iteratively calculates the data of different groups in the training set.

[0056] In conjunction with the second aspect, in some embodiments, the apparatus is configured to:

[0057] When the preset conditions are met, the training of the original neural network training model is stopped and the trained neural network training model is output, including:

[0058] The controller inputs a set of training data, including an original thickness, an actual thickness, and a target size, into the original neural network training model, and obtains the predicted wear parameter value output by the original neural network training model;

[0059] The controller determines the error value based on the predicted wear parameter value output by the initial neural network training model and the wear parameter value in the training data;

[0060] When the error value is less than the preset error value, the training is stopped and the trained neural network training model is output.

[0061] In conjunction with the second aspect, in some embodiments, the apparatus is configured to:

[0062] The controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value, including:

[0063] When the wear parameter value is less than the preset wear parameter value, the controller does not control the relative angle between the cutting edge and the lower die base to change;

[0064] When the wear parameter value is greater than or equal to the first preset wear parameter value, the larger the wear parameter value is, the larger the relative angle between the cutting edge and the lower die seat controlled by the controller is;

[0065] When the wear parameter value is greater than or equal to the second preset wear parameter value, the controller controls the shearing die system to stop working and sends an alarm signal through the alarm system.

[0066] In conjunction with the second aspect, in some embodiments, the apparatus is configured to:

[0067] When the wear parameter value is greater than or equal to the first preset wear parameter value, the larger the wear parameter value is, the larger the relative angle between the cutting edge and the lower die seat controlled by the controller is, satisfying:

[0068]

[0069] Where α is the relative angle between the cutting edge and the lower die seat, K is the wear parameter value, Q is a constant, h is the original thickness, H is the hardness of the object to be sheared, and M is the yield strength of the object to be sheared.

[0070] In conjunction with the second aspect, in some embodiments, the apparatus is configured to:

[0071] The controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value, including:

[0072] The controller controls the relative angle between the lower die base and the cutting edge to change based on the relative angle between the cutting edge and the lower die base.

[0073] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including:

[0074] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method proposed in the first aspect of the embodiment of the present invention.

[0075] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the embodiment of the present invention.

[0076] In summary, the above method and device have the following technical effects:

[0077] The embodiment of the present application proposes a high-reliability mold automation control method and device. After the controller controls the conveying system to send the object to be cut into the shearing system, the controller controls the shearing system to shear the object to be cut so that the object to be cut forms a target product. Then, the controller controls the conveying system to send the object to be cut into the detection system. The detection system is used to detect the actual size of the target product. Then, the controller determines the wear parameter value of the cutting edge in the shearing system based on the actual size. The wear parameter value is used to characterize the degree of wear of the cutting edge. Finally, the controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value. The embodiment of the present application proposes a high-reliability mold automation control method and device. The controller controls the actual size of the target product through the detection system, determines the wear parameter value of the cutting edge through the actual size, and adjusts the relative angle between the cutting edge and the lower die seat according to the wear parameter value of the cutting edge so that the size of the actual product is less affected by the wear of the cutting edge. No personnel are required to make adjustments at any time, thereby improving the reliability and automation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 This is a flow chart of a high-reliability mold automation control method proposed in this embodiment.

[0079] Figure 2 This is a partial structural diagram of a shearing system proposed in this embodiment.

[0080] The accompanying drawings are numerals as follows:

[0081] 1-Cutting edge; 2-Object to be sheared; 3-Lower die base; 4-Target product; 5-Fixed part. DETAILED DESCRIPTION

[0082] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0083] The embodiment of the present application proposes a high-reliability mold automation control method applicable to a shearing mold system, which includes a transmission system, a shearing system, a detection system, and a controller. The shearing system includes a lower mold base and a cutting edge that moves relative to the lower mold base. Figure 1 , the method comprises the following steps:

[0084] S101: The controller controls the conveying system to send the object to be sheared into the shearing system.

[0085] It can be understood that in this embodiment, the conveying system can be a conveying system such as a conveyor belt, or a positioning or non-positioning conveying system. The conveying system is used to convey objects to be sheared and provide a detection plane during subsequent detection. The specific structure of the conveying system is not limited in this embodiment.

[0086] In this embodiment, the controller is in communication with the conveying system and is used to control the conveying system to perform conveying operations. In this step, the controller conveys the object to be sheared into the shearing system via the conveying system and shears the object. In this embodiment, the object to be sheared is a plate-like structure, such as a copper plate or a steel plate.

[0087] S102: The controller controls the shearing system to shear the object to be sheared so that the object to be sheared forms a target product.

[0088] It is understood that in this embodiment, the shearing system is used to perform single-line shearing on the object 2 to be sheared. It is understood that during the shearing process, the lower die base and the cutting edge 1 squeeze and deform the object 2 to be sheared according to the specified position, see Figure 2 , partially separating the object 2 to be sheared from the target product 4. Ideally, the relative angle β between the lower die holder 3 and the cutting edge 1 is perpendicular, ensuring that the sheared target product 4 has a bright band that meets preset requirements. In some embodiments, the lower die holder 3 is further provided with a fixing portion 5 for securing a portion of the object 2 to be sheared during the shearing process. The method for delivering the object 1 to be sheared into the shearing system via a conveyor system is disclosed in relevant patents and is not limited in this embodiment.

[0089] S103: The controller controls the conveying system to send the object to be sheared into the detection system, which is used to detect the actual size of the target product.

[0090] It can be understood that in this step, the detection system can be an optical detection system using methods such as image recognition or laser positioning, or a mechanical detection system. There is no limitation on how to detect in this embodiment.

[0091] It is understandable that in actual situations, after the object to be sheared is subjected to a force in the direction of the cutting edge, the actual height h will change and be greater than the original height.

[0092] S104: The controller determines the wear parameter value of the cutting edge in the shearing system based on the actual size. The wear parameter value is used to characterize the degree of wear of the cutting edge.

[0093] It is understandable that after the cutting edge is worn, the length of the sheared bright band is less than the original length, and the actual size after shearing will also change. There is a certain mathematical relationship between these changed data and the degree of cutting edge wear, but due to various environmental factors, material factors, etc., it cannot be described by a simple mathematical equation. Therefore, as an implementation method, the data obtained by the detection system and the artificial intelligence model can be trained and calculated to a certain extent to achieve a certain degree of representation of cutting edge wear. It is understandable that in this embodiment, the wear parameter value of the cutting edge is used to characterize the degree of cutting edge wear.

[0094] Specifically, as an implementation, step S104 may include the following steps:

[0095] S1041: The controller obtains the original thickness of the object to be cut, and obtains the actual thickness and target size of the target product based on the detection system, where the target size is the length from the cutting edge cutting position to the edge of the target product perpendicular to the cutting edge direction, and the actual thickness is the distance from the highest point of the target product to the ideal plane when it is in the ideal plane.

[0096] Understandable, please continue reading Figure 2 , the target size is Figure 2 The length L shown in FIG. This length can be detected by a detection system. At the same time, the actual thickness can also be measured and obtained by a measurement system. The specific measurement process is not limited in this embodiment.

[0097] S1042: The controller inputs the original thickness, actual thickness and target size into the trained neural network training model. The neural network training model is used to output the predicted wear parameter value based on the input original thickness, actual thickness and target size.

[0098] As you can understand, the controller feeds a carefully selected set of raw thickness data, actual thickness data, and target dimensions into a fully trained neural network model. This neural network model is trained using a large amount of data and complex algorithms, accurately capturing the complex relationships between the input data. In this way, the controller can use this well-trained neural network model to predict the corresponding wear parameter values ​​based on the input raw thickness, actual thickness, and target dimensions. These wear parameter values ​​are of great reference value for equipment maintenance and optimization, helping engineers better understand the wear of the equipment and take appropriate measures to extend its service life and improve its operating efficiency.

[0099] Regarding how to obtain the training model, in some embodiments, it can be obtained by iterative calculation based on a training set consisting of a large amount of data. Specifically, as an implementation method, the controller inputs the original thickness, actual thickness, and target size into a trained neural network training model. The neural network training model is used to output a predicted wear parameter value based on the input original thickness, actual thickness, and target size, including:

[0100] S201: The controller obtains a training set, which includes multiple groups of training data, wherein a group of training data includes at least one original thickness, an actual thickness, a target size, and a wear parameter value.

[0101] It is understandable that the training data may be data recorded after multiple operations, or may be directly input data, which is not limited in this embodiment.

[0102] S202: The controller inputs the training set into the original neural network training model, which is used to iteratively calculate an original thickness, an actual thickness, a target size, and a wear parameter value.

[0103] S203: When the preset conditions are met, the training of the original neural network training model is stopped and the trained neural network training model is output.

[0104] As you can understand, in this step, the controller is responsible for inputting the training set data into a pre-set original neural network training model. The main function of this original neural network training model is to perform iterative calculations. Specifically, it processes a series of input parameters, including but not limited to a specific original thickness value, a corresponding actual thickness value, a target size value, and a wear parameter value. Through repeated iterative calculations of these input parameters, the model can gradually optimize its internal weights and structure, thereby improving its ability to predict and analyze data. During the training process, the system continuously monitors whether certain preset conditions are met. This condition may include reaching a certain number of iterations, the model error reaching a certain threshold, or the training time exceeding a predetermined limit. Once any of these preset conditions is met, the system will stop further training of the original neural network training model. At this point, the trained neural network model will be output, indicating that the model has the ability to handle real-world problems and can be used for subsequent prediction, classification, or other related tasks.

[0105] Specifically, as a feasible implementation method, the controller takes an original thickness, an actual thickness, and a target size as three different dimensions. The three different dimensions constitute the target vector of the wear parameter value. Then, the controller iteratively calculates different groups of data in the training set.

[0106] Regarding how to stop the training process, as an implementation method, it can be calculated by error value determination. In this embodiment, the error value determination method is taken as an example. In other embodiments, there can also be other determination methods, which are not limited in this embodiment.

[0107] In this embodiment, as an implementation method, the controller inputs an original thickness, an actual thickness, and a target size from a set of training data into the original neural network training model, and obtains the predicted wear parameter value output by the original neural network training model. Then, the controller determines the error value based on the predicted wear parameter value output by the original neural network training model and the wear parameter value in the training data. Finally, when the error value is less than the preset error value, the training is stopped and the trained neural network training model is output.

[0108] S1043: The controller confirms the wear parameter value based on the output results of the neural network training model.

[0109] It can be understood that the output result of the neural network training model is the wear parameter value predicted based on the original thickness, actual thickness, and target size.

[0110] S105: The controller controls the relative angle between the cutting edge and the lower die base based on the wear parameter value.

[0111] It is understandable that, in actual use, since the actual moving component is the cutting edge and its connected components, adjusting the cutting edge is more inconvenient than adjusting the lower die base. Therefore, in this embodiment, the controller can control the relative angle between the lower die base and the cutting edge based on the relative angle between the cutting edge and the lower die base. In other embodiments, different methods can also be used to adjust the angle of the cutting edge, which is not limited in this application.

[0112] It can be understood that after obtaining the predicted wear parameter value, the angle between the cutting edge and the lower die base can be adaptively adjusted according to the wear parameter value. By adjusting the angle between the cutting edge and the lower die base, the final product size can be changed to ensure that after wear, the actual product size can meet the requirements.

[0113] Of course, in other embodiments, after the wear parameter value is determined, a direct warning or display may be given, for example, through a display device, to prompt the operator to perform replacement.

[0114] In this embodiment, the angle between the cutting edge and the lower die seat can be adjusted according to the wear parameter value in the following manner:

[0115] S1051: When the wear parameter value is less than the preset wear parameter value, the controller does not control the relative angle between the cutting edge and the lower die base to change.

[0116] It is understandable that when the wear parameter value is less than a certain range, combined with the accuracy and error range of the time product, there is no need to regulate the specific shearing process.

[0117] S1052: When the wear parameter value is greater than or equal to the first preset wear parameter value, the larger the wear parameter value is, the larger the relative angle between the cutting edge and the lower die base controlled by the controller is.

[0118] Specifically, in this embodiment, the following conditions are met:

[0119]

[0120] Where α is the relative angle change between the cutting edge and the lower die seat, K is the wear parameter value, Q is a constant, h is the original thickness, H is the hardness of the object to be sheared, and M is the yield strength of the object to be sheared.

[0121] S1053: When the wear parameter value is greater than or equal to the second preset wear parameter value, the controller controls the shearing die system to stop working and sends an alarm signal through the alarm system.

[0122] It can be understood that in this embodiment, when the wear parameter value is greater than or equal to the second preset wear parameter value, the adjustment limit has been reached. At this time, an alarm signal can be issued by the alarm system to prompt the user to change the tool.

[0123] The embodiment of the present application proposes a high-reliability mold automation control method. After the controller controls the conveying system to send the object to be cut into the shearing system, the controller controls the shearing system to shear the object to be cut so that the object to be cut forms a target product. Then, the controller controls the conveying system to send the object to be cut into the detection system. The detection system is used to detect the actual size of the target product. Then, the controller determines the wear parameter value of the cutting edge in the shearing system based on the actual size. The wear parameter value is used to characterize the degree of wear of the cutting edge. Finally, the controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value. The embodiment of the present application proposes a high-reliability mold automation control method. The controller controls the conveying system to send the object to be cut into the detection system. The detection system is used to detect the actual size of the target product. The wear parameter value is used to characterize the degree of wear of the cutting edge. Finally, the controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value. The embodiment of the present application proposes a high-reliability mold automation control method. The controller controls the conveying system to send the object to be cut into the detection system. The detection system is used to detect the actual size of the target product. The wear parameter value of the cutting edge is used to characterize the degree of wear of the cutting edge. Finally, the controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value.

[0124] Based on the same inventive concept, this application proposes a high-reliability mold automation control device applicable to a shearing mold system, which includes a conveying system, a shearing system, a detection system, and a controller. The shearing system includes a lower mold base and a cutting edge that moves relative to the lower mold base. The device is configured as follows:

[0125] The controller controls the conveying system to deliver the objects to be sheared into the shearing system;

[0126] The controller controls the shearing system to shear the object to be sheared so that the object to be sheared forms a target product;

[0127] The controller controls the conveying system to send the object to be sheared into the detection system, which is used to detect the actual size of the target product;

[0128] The controller determines the wear parameter value of the cutting edge in the shearing system based on the actual size. The wear parameter value is used to characterize the degree of wear of the cutting edge.

[0129] The controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value.

[0130] In some embodiments, the apparatus is configured to:

[0131] Based on the actual size, the controller determines the wear parameter value of the cutting edge in the shearing system. The wear parameter value is used to characterize the degree of wear of the cutting edge, including:

[0132] The controller obtains the original thickness of the object to be sheared and, based on the detection system, obtains the actual thickness and target size of the target product. The target size is the length from the cutting position of the cutting edge to the edge of the target product, perpendicular to the cutting edge. The actual thickness is the distance from the highest point of the target product to the ideal plane when it is on the ideal plane.

[0133] The controller inputs the original thickness, actual thickness and target size into a trained neural network training model, and the neural network training model is used to output a predicted wear parameter value based on the input original thickness, actual thickness and target size;

[0134] The controller confirms the wear parameter value based on the output results of the neural network training model.

[0135] In some embodiments, the apparatus is configured to:

[0136] The controller inputs the original thickness, actual thickness, and target size into the trained neural network training model. The neural network training model is used to output the predicted wear parameter value based on the input original thickness, actual thickness, and target size, including:

[0137] The controller obtains a training set, the training set including multiple sets of training data, wherein a set of training data includes at least one original thickness, an actual thickness, a target size, and a wear parameter value;

[0138] The controller inputs the training set into the original neural network training model, and the original neural network training model is used to iteratively calculate an original thickness, an actual thickness, a target size, and a wear parameter value;

[0139] When the preset conditions are met, the training of the original neural network training model is stopped and the trained neural network training model is output.

[0140] In some embodiments, the apparatus is configured to:

[0141] The controller inputs the training set into the original neural network training model, which is used to iteratively calculate an original thickness, an actual thickness, a target size, and a wear parameter value, including:

[0142] The controller takes an original thickness, an actual thickness, and a target size as three different dimensions, and the three different dimensions constitute the target vector of the wear parameter value;

[0143] The controller iteratively calculates the data of different groups in the training set.

[0144] In some embodiments, the apparatus is configured to:

[0145] When the preset conditions are met, the training of the original neural network training model is stopped and the trained neural network training model is output, including:

[0146] The controller inputs a set of training data, including an original thickness, an actual thickness, and a target size, into the original neural network training model, and obtains the predicted wear parameter value output by the original neural network training model;

[0147] The controller determines the error value based on the predicted wear parameter value output by the initial neural network training model and the wear parameter value in the training data;

[0148] When the error value is less than the preset error value, the training is stopped and the trained neural network training model is output.

[0149] In some embodiments, the apparatus is configured to:

[0150] The controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value, including:

[0151] When the wear parameter value is less than the preset wear parameter value, the controller does not control the relative angle between the cutting edge and the lower die base to change;

[0152] When the wear parameter value is greater than or equal to the first preset wear parameter value, the larger the wear parameter value is, the larger the relative angle between the cutting edge and the lower die seat controlled by the controller is;

[0153] When the wear parameter value is greater than or equal to the second preset wear parameter value, the controller controls the shearing die system to stop working and sends an alarm signal through the alarm system.

[0154] In some embodiments, the apparatus is configured to:

[0155] When the wear parameter value is greater than or equal to the first preset wear parameter value, the larger the wear parameter value is, the larger the relative angle between the cutting edge and the lower die seat controlled by the controller is, satisfying:

[0156]

[0157] Where α is the relative angle between the cutting edge and the lower die seat, K is the wear parameter value, Q is a constant, h is the original thickness, H is the hardness of the object to be sheared, and M is the yield strength of the object to be sheared.

[0158] In some embodiments, the apparatus is configured to:

[0159] The controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value, including:

[0160] The controller controls the relative angle between the lower die base and the cutting edge to change based on the relative angle between the cutting edge and the lower die base.

[0161] The embodiment of the present application proposes a high-reliability mold automation control device. After the controller controls the conveying system to send the object to be cut into the shearing system, the controller controls the shearing system to shear the object to be cut so that the object to be cut forms a target product. Then, the controller controls the conveying system to send the object to be cut into the detection system. The detection system is used to detect the actual size of the target product. Then, the controller determines the wear parameter value of the cutting edge in the shearing system based on the actual size. The wear parameter value is used to characterize the degree of wear of the cutting edge. Finally, the controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value. The embodiment of the present application proposes a high-reliability mold automation control device. The controller controls the actual size of the target product through the detection system, determines the wear parameter value of the cutting edge through the actual size, and adjusts the relative angle between the cutting edge and the lower die seat according to the wear parameter value of the cutting edge so that the size of the actual product is less affected by the wear of the cutting edge. No personnel are required to make adjustments at any time, thereby improving the reliability and automation of the equipment.

[0162] Based on the same inventive concept, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0163] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the high-reliability mold automation control method of an embodiment of the present application.

[0164] In addition, to achieve the above-mentioned purpose, an embodiment of the present application also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the high-reliability mold automation control method of the embodiment of the present application.

[0165] The following is a detailed introduction to the various components of electronic equipment:

[0166] The term "processor" is the control center of an electronic device and may be a single processor or a collective term for multiple processing elements. For example, the processor may be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0167] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0168] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0169] Alternatively, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processor through an interface circuit of the electronic device, and this is not specifically limited in the embodiments of the present invention.

[0170] A transceiver is used to communicate with network devices or terminal devices.

[0171] Optionally, the transceiver may include a receiver and a transmitter, wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0172] Optionally, the transceiver may be integrated with the processor, or may exist independently and be coupled to the processor via an interface circuit of the router, which is not specifically limited in the embodiment of the present invention.

[0173] In addition, the technical effects of the electronic device can refer to the technical effects of the data transmission method in the above method embodiment, and will not be repeated here.

[0174] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0175] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0176] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function according to the embodiments of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available media can be magnetic media (such as floppy disks, hard disks, tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0177] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0178] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0179] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0180] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

Claims

1. A high reliability mold automation control method, characterized in that: Applicable to a shearing die system, the shearing die system includes a conveying system, a shearing system, a detection system and a controller, the shearing system includes a lower die base and a cutting edge that moves relative to the lower die base, the method includes: The controller controls the conveying system to deliver the object to be sheared into the shearing system; The controller controls the shearing system to shear the object to be sheared so that the object to be sheared forms a target product; The controller controls the conveying system to send the object to be sheared into the detection system, and the detection system is used to detect the actual size of the target product; The controller determines a wear parameter value of the cutting edge in the shearing system based on the actual size, wherein the wear parameter value is used to characterize the degree of wear of the cutting edge. The controller obtains the original thickness of the object to be sheared, and obtains the actual thickness and target size of the target product based on the detection system, wherein the target size is the length from the cutting position of the cutting edge to the edge of the target product perpendicular to the cutting edge, and the actual thickness is the distance from the highest point of the target product to the ideal plane when the target product is on the ideal plane; The controller inputs the original thickness, the actual thickness, and the target size into a trained neural network training model, and the neural network training model is used to output a predicted wear parameter value based on the input original thickness, the actual thickness, and the target size; The controller confirms the wear parameter value according to the output result of the neural network training model; The controller controls a relative angle between the cutting edge and the lower die seat based on the wear parameter value.

2. A high reliability mold automation control method according to claim 1, characterized in that: The controller inputs the original thickness, the actual thickness, and the target size into a trained neural network training model. Before the neural network training model outputs a predicted wear parameter value based on the input original thickness, the actual thickness, and the target size, the controller includes: The controller obtains a training set, the training set including multiple sets of training data, wherein a set of the training data includes at least one of the original thickness, the actual thickness, the target size, and the wear parameter value; The controller inputs the training set into an original neural network training model, and the original neural network training model is used to iteratively calculate the original thickness, the actual thickness, the target size, and the wear parameter value; When a preset condition is met, the training of the original neural network training model is stopped and the trained neural network training model is output.

3. A high reliability mold automation control method according to claim 2, characterized in that: The controller inputs the training set into an original neural network training model, and the original neural network training model is used to iteratively calculate the original thickness, the actual thickness, the target size, and the wear parameter value, including: The controller uses the original thickness, the actual thickness, and the target size as three different dimensions, and the three different dimensions constitute the target vector of the wear parameter value; The controller performs iterative calculations on different groups of data in the training set.

4. A high reliability mold automation control method according to claim 3, characterized in that: When a preset condition is met, the training of the original neural network training model is stopped and the trained neural network training model is output, including: The controller inputs one of the original thickness, one of the actual thickness, and one of the target size in a set of the training data into the original neural network training model, and obtains the predicted wear parameter value output by the original neural network training model; The controller determines an error value based on the predicted wear parameter value output by the initial neural network training model and the wear parameter value in the training data; When the error value is less than the preset error value, the training is stopped and the trained neural network training model is output.

5. The high reliability mold automation control method according to claim 1, characterized in that: The controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value, including: When the wear parameter value is less than a preset wear parameter value, the controller does not control the relative angle between the cutting edge and the lower die base to change; When the wear parameter value is greater than or equal to a first preset wear parameter value, the larger the wear parameter value is, the larger the relative angle between the cutting edge and the lower die seat is controlled by the controller; When the wear parameter value is greater than or equal to a second preset wear parameter value, the controller controls the shearing die system to stop working and sends an alarm signal through the alarm system.

6. A high reliability mold automation control method according to claim 5, characterized in that: When the wear parameter value is greater than or equal to the first preset wear parameter value, the larger the wear parameter value is, the larger the relative angle between the cutting edge and the lower die seat is controlled by the controller to be, satisfying: Wherein, α is the relative angle change between the cutting edge and the lower die seat, K is the wear parameter value, Q is a constant, h is the original thickness, H is the hardness of the object to be sheared, and M is the yield strength of the object to be sheared.

7. A high reliability mold automation control method according to claim 1, characterized in that: The controller controls the relative angle between the cutting edge and the lower die seat based on the wear parameter value, including: The controller controls the relative angle between the lower die base and the cutting edge to change based on the relative angle between the cutting edge and the lower die base.

8. A high reliability mold automation control device, characterized in that: Applicable to a shearing die system, the shearing die system includes a conveying system, a shearing system, a detection system and a controller, the shearing system includes a lower die base and a cutting edge that moves relative to the lower die base, the device is configured as follows: The controller controls the conveying system to deliver the object to be sheared into the shearing system; The controller controls the shearing system to shear the object to be sheared so that the object to be sheared forms a target product; The controller controls the conveying system to send the object to be sheared into the detection system, and the detection system is used to detect the actual size of the target product; The controller determines a wear parameter value of the cutting edge in the shearing system based on the actual size, wherein the wear parameter value is used to characterize the degree of wear of the cutting edge. The controller obtains the original thickness of the object to be sheared, and obtains the actual thickness and target size of the target product based on the detection system, wherein the target size is the length from the cutting position of the cutting edge to the edge of the target product perpendicular to the cutting edge, and the actual thickness is the distance from the highest point of the target product to the ideal plane when the target product is on the ideal plane; The controller inputs the original thickness, the actual thickness, and the target size into a trained neural network training model, and the neural network training model is used to output a predicted wear parameter value based on the input original thickness, the actual thickness, and the target size; The controller confirms the wear parameter value according to the output result of the neural network training model; The controller controls a relative angle between the cutting edge and the lower die seat based on the wear parameter value.

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable the at least one processor to perform the method as claimed in any one of claims 1 to 7.

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