Stamping part quality optimization method, system and device based on artificial intelligence

Abstract

The invention discloses a stamping part quality optimization method, system and device based on artificial intelligence, and relates to the technical field of electric digital data processing. The stamping part quality optimization method based on artificial intelligence comprises the following steps that a stamping product work station transmits evaluation; stamping product detection quality evaluation; and storage environment control evaluation. According to the method, whether work station transmission optimization is carried out or not is judged through the stamping product work station transmission evaluation value, and then after the stamping product work station transmission evaluation value meets the work station transmission condition, whether the specified stamping product meets the detection quality condition or not is judged according to the stamping product detection quality evaluation value; and finally, storage environment control evaluation is performed to obtain a storage environment control evaluation value, and whether unmanned warehouse environment control parameter adjustment is performed is judged, so that the effect of improving the accuracy of quality optimization analysis on the notebook computer shell is achieved. The problem that in the prior art, quality optimization analysis on the notebook computer shell based on artificial intelligence is insufficient is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a stamping part quality optimization method, system and device based on artificial intelligence. Background Art

[0002] In recent years, artificial intelligence technology has made significant progress, especially in the fields of machine learning and deep learning. These technologies have powerful data processing and analysis capabilities, can extract valuable information from large amounts of data, and provide unprecedented optimization means for the manufacturing industry. The rapid development of artificial intelligence technology has provided new ideas and methods for optimizing the quality of stamping parts. With the continuous growth of the laptop market, especially the strong market demand for laptops with a screen size of 13.0-14.9 inches, the traditional stamping method is far from meeting the needs of the market. Artificial intelligence can establish an intelligent prediction model for stamping process by analyzing data such as stamping equipment processing parameters, sheet parameters, mold performance parameters and maintenance records. The model can accurately predict possible defects in stamping parts, such as cracking and springback, thereby providing strong support for process optimization.

[0003] The existing system is mainly based on experimental data, theoretical models and numerical simulations, taking into account material properties, cutting parameters, and mechanical and thermal behaviors during the cutting process, and using a combination of experimental, theoretical and numerical simulation methods to construct an alloy cutting model.

[0004] For example, the invention patent application with publication number: CN116151087A discloses a method, device, equipment and computer program product for optimizing the quality of stamping parts, including: receiving one or more material parameters, one or more process parameters and defect data; generating an association model based at least on the defect data, one or more material parameters, and one or more process parameters; and predicting one or more potential defects in one or more additional stamping parts based at least on the association model.

[0005] For example, a method for predicting quality defects on the outer surface of a stamping part is disclosed in the invention patent with the publication number CN107491602B, and includes: S10, obtaining a unit grid A after the stamping part is formed through finite element simulation; S20, mapping the unit grid A to the geometric surface of the mold profile to obtain a mapping grid B; S30, performing thickness offset on the unit grid A according to the thickness value of the unit grid A, and obtaining an outer surface contour grid C; S40, taking the mapping grid B as the deformation starting point and the outer surface contour grid C as the target end point, and obtaining the outer surface curve of the stamping part by using the geometric surface of the mold profile through a topological geometry algorithm; S50, inspecting the outer surface curve of the stamping part through a geometric surface quality inspection method.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the existing technology, the transmission and inspection of traditional stamping products between different workstations rely entirely on manual labor, which is not only inefficient, but also due to the large errors in manual inspection, it is difficult to ensure the quality stability and consistency of stamping products during the production process, resulting in insufficient quality optimization analysis of notebook shells based on artificial intelligence. Summary of the invention

[0007] The embodiments of the present application solve the problem of insufficient quality optimization analysis of notebook shells based on artificial intelligence in the prior art by providing a stamping part quality optimization method, system and device based on artificial intelligence, thereby improving the accuracy of quality optimization analysis of notebook shells.

[0008] The embodiment of the present application provides a stamping parts quality optimization method based on artificial intelligence, comprising the following steps: S1, obtaining a stamping product station transfer evaluation value based on the obtained station transfer data of a specified stamping product, judging whether to perform station transfer optimization based on the stamping product station transfer evaluation value, the stamping product station transfer evaluation value is used to evaluate the transfer situation of the specified stamping product in the station transfer process, and the station transfer optimization is used to improve the station transfer quality of the specified stamping product; S2, after the stamping product station transfer evaluation value meets the station transfer condition, a dimensional inspection quality evaluation is performed based on the obtained stamping product dimensional inspection data to obtain a stamping product inspection quality evaluation value, and the stamping product inspection quality evaluation value is used to evaluate the inspection quality during the dimensional inspection process of the specified stamping product; S3, judging whether the specified stamping product meets the inspection quality condition based on the stamping product inspection quality evaluation value, if it meets the inspection quality condition, a storage environment control evaluation is performed based on the obtained unmanned warehouse inspection data to obtain a storage environment control evaluation value, judging whether to adjust the unmanned warehouse environment control parameters based on the storage environment control evaluation value, and the storage environment control evaluation value is used to evaluate the storage environment intelligent control situation of the specified stamping product.

[0009] Furthermore, the workstation transmission data includes the conveyor belt movement speed, the manipulator movement speed and the manipulator position coordinates; the manipulator position coordinates include the manipulator first position coordinates, the manipulator second position coordinates and the manipulator third position coordinates; the stamping product size detection data includes image resolution, specified stamping product length, specified stamping product width, specified stamping product thickness and product position coordinates; the product position coordinates include the product first position coordinates, the product second position coordinates and the product third position coordinates; the unmanned warehouse detection data includes the unmanned warehouse temperature difference, the unmanned warehouse humidity difference, the shelf pressure and the specified stamping product length change; the manipulator first The first position coordinate represents the horizontal coordinate of the manipulator end effector in the preset three-dimensional rectangular coordinate system; the second position coordinate of the manipulator represents the vertical coordinate of the manipulator end effector in the preset three-dimensional rectangular coordinate system; the third position coordinate of the manipulator represents the coordinate of the manipulator end effector in the preset three-dimensional rectangular coordinate system perpendicular to the horizontal axis and the vertical axis in the plane formed by the preset three-dimensional rectangular coordinate system; the first position coordinate of the product represents the horizontal coordinate of the specified stamping product in the preset three-dimensional rectangular coordinate system; the second position coordinate of the product represents the vertical coordinate of the specified stamping product in the preset three-dimensional rectangular coordinate system; the third position coordinate of the product represents the coordinate of the specified stamping product in the plane formed by the preset three-dimensional rectangular coordinate system perpendicular to the horizontal axis and the vertical axis.

[0010] Furthermore, the specific process of obtaining the stamping product station transfer evaluation value based on the obtained station transfer data of the specified stamping product is as follows: obtain the conveyor belt moving speed evaluation value by performing a ratio operation on the conveyor belt moving speed and the preset conveyor belt moving speed threshold obtained from the database and then performing an integral process; obtain the manipulator moving speed evaluation value, the manipulator moving speed evaluation value is obtained by performing a ratio operation on the sum of the manipulator moving speed and the preset manipulator moving speed threshold obtained from the database and then performing an integral process; obtain the first positioning accuracy evaluation value by processing the manipulator first position coordinates and the preset manipulator final state first coordinates obtained from the database; obtain the first positioning accuracy evaluation value by processing the manipulator second position coordinates and the preset manipulator final state second coordinates obtained from the database A second positioning accuracy evaluation value is obtained; a third positioning accuracy evaluation value is obtained by processing the third position coordinates of the manipulator and the third coordinates of the preset manipulator final state obtained from the database; when the conveyor belt moving speed evaluation value is within the preset conveyor belt moving speed range obtained from the database, the stamping product station transfer evaluation value is obtained through the obtained conveyor belt moving speed evaluation value, the manipulator moving speed evaluation value, the first positioning accuracy evaluation value, the second positioning accuracy evaluation value and the third positioning accuracy evaluation value; when the conveyor belt moving speed evaluation value is not within the preset conveyor belt moving speed range obtained from the database, the conveyor belt moving speed evaluation value is recorded as 0, and the stamping product station transfer evaluation value is obtained through the obtained manipulator moving speed evaluation value, the first positioning accuracy evaluation value, the second positioning accuracy evaluation value and the third positioning accuracy evaluation value.

[0011] Furthermore, the limiting expression of the transfer evaluation value of the stamping product station is as follows: ; In the formula, Indicates the transfer evaluation value of stamping product station. It represents the estimated value of the conveyor belt moving speed. Indicates the estimated value of the robot's moving speed. represents the first positioning accuracy evaluation value, represents the second positioning accuracy evaluation value, represents the third positioning accuracy evaluation value, e represents a natural constant, Indicates the preset conveyor belt moving speed range.

[0012] Furthermore, the specific process of judging whether to perform station transfer optimization based on the stamping product station transfer evaluation value is as follows: when the stamping product station transfer evaluation value meets the station transfer conditions, it indicates that the designated stamping product station transfer is qualified and the station transfer optimization is not performed; when the stamping product station transfer evaluation value does not meet the station transfer conditions, it indicates that the designated stamping product station transfer is unqualified and the station transfer optimization is performed; when the stamping product station transfer evaluation value still does not meet the station transfer conditions after the station transfer optimization is performed, an alarm is sent to the preset personnel; the station transfer optimization includes manipulator preload and conveyor belt traction adjustment; the station transfer condition indicates that the stamping product station transfer evaluation value is not lower than the preset station transfer average threshold obtained from the database.

[0013] Furthermore, the specific process of obtaining the stamping product inspection quality assessment value is as follows: obtain a resolution assessment value, which is obtained by performing an integration process after performing a ratio operation on the sum of the image resolution obtained from the database and the preset resolution threshold and twice the preset resolution threshold; obtain a length error assessment value, which is obtained by processing the specified stamping product length and the preset length threshold obtained from the database; obtain a width error assessment value, which is obtained by processing the specified stamping product width and the preset width threshold obtained from the database; obtain a thickness error assessment value, which is obtained by processing the specified stamping product thickness and the preset thickness threshold obtained from the database; obtain a product positioning accuracy assessment value, which is obtained by processing the product position coordinates and the preset product position coordinates obtained from the database and the preset positioning accuracy assessment threshold; obtain the stamping product inspection quality assessment value through the obtained resolution assessment value, length error assessment value, width error assessment value, thickness error assessment value and product positioning accuracy assessment value; the preset product position coordinates include the preset product first final state position coordinates, the preset product second final state position coordinates, and the preset product third final state position coordinates.

[0014] Furthermore, the specific process of judging whether a specified stamping product meets the inspection quality conditions based on the stamping product inspection quality evaluation value is as follows: when the stamping product inspection quality evaluation value meets the inspection quality conditions, it indicates that the stamping product inspection quality is qualified, and mold parameter optimization is not performed; when the stamping product inspection quality evaluation value does not meet the inspection quality conditions, it indicates that the stamping product inspection quality is unqualified, and mold parameter optimization is performed; after the mold parameter optimization, the stamping product inspection quality evaluation value still does not meet the inspection quality conditions, and an alarm is sent to the preset personnel; the mold parameter optimization indicates sending a prompt to the preset personnel to change the side wall height, R angle, side wall angle and side wall thickness of the mold corresponding to the specified stamping product; the inspection quality condition indicates that the stamping product inspection quality evaluation value is not lower than the preset inspection quality threshold obtained from the database.

[0015] Furthermore, the specific process of judging whether to adjust the environmental control parameters of the unmanned warehouse based on the storage environment control evaluation value is as follows: the temperature difference of the unmanned warehouse and the preset temperature difference threshold obtained from the database are processed to obtain the temperature difference-length change evaluation value; the humidity difference of the unmanned warehouse and the preset humidity difference threshold obtained from the database are processed to obtain the humidity difference-length change evaluation value; the shelf pressure and the preset shelf pressure threshold obtained from the database are processed to obtain the pressure-length change evaluation value; the storage environment control is obtained through the length change impact value, the temperature difference-length change evaluation value, the humidity difference-length change evaluation value and the pressure-length change evaluation value. Evaluation value; the length change impact value is obtained by performing hyperbolic tangent function processing on the result of the ratio operation of the sum of the specified stamping product length change and the preset length change threshold obtained from the database and twice the preset length change threshold; judging whether the storage environment control evaluation value is not lower than the preset storage environment control threshold obtained from the database, when the storage environment control evaluation value is not lower than the preset storage environment control threshold, it indicates that the storage environment control is qualified, and the unmanned warehouse environment control parameters are not adjusted; when the storage environment control evaluation value is lower than the preset storage environment control threshold, it indicates that the storage environment control is unqualified, and a prompt is sent to the preset personnel to adjust the unmanned warehouse environment control parameters.

[0016] An embodiment of the present application provides an artificial intelligence-based stamping parts quality optimization system, including a stamping product station transfer evaluation module, a stamping product inspection quality evaluation module and a storage environment control evaluation module: wherein the stamping product station transfer evaluation module is used to obtain a stamping product station transfer evaluation value based on the acquired station transfer data of a specified stamping product, and judge whether to perform station transfer optimization based on the stamping product station transfer evaluation value; the stamping product station transfer evaluation value is used to evaluate the transfer of a specified stamping product during the station transfer process; the station transfer optimization is used to improve the station transfer quality of a specified stamping product; the stamping product inspection quality evaluation module is used to determine whether to perform station transfer optimization when the stamping product station transfer evaluation value meets the station transfer quality requirements; After the conditions are passed, a dimensional inspection quality assessment is performed based on the acquired stamping product dimensional inspection data to obtain a stamping product inspection quality assessment value, and the stamping product inspection quality assessment value is used to evaluate the inspection quality during the dimensional inspection of the designated stamping product; the storage environment control assessment module is used to determine whether the designated stamping product meets the inspection quality conditions based on the stamping product inspection quality assessment value, and if it meets the inspection quality conditions, a storage environment control assessment is performed based on the acquired unmanned warehouse inspection data to obtain a storage environment control assessment value, and based on the storage environment control assessment value, it is determined whether to adjust the unmanned warehouse environment control parameters, and the storage environment control assessment value is used to evaluate the storage environment intelligent control status of the designated stamping product.

[0017] An embodiment of the present application provides an apparatus for applying the artificial intelligence-based stamping parts quality optimization method as described above, characterized in that it includes stamping parts quality optimization equipment, a processor and a memory; the stamping parts quality optimization equipment includes a stamping machine, a material rack, a four-axis robot, a pallet and an intelligent sensor; the intelligent sensor includes a speed sensor, a temperature sensor, a humidity sensor and a pressure sensor; the processor is used to obtain a stamping product station transfer evaluation value, a stamping product inspection quality evaluation value and a storage environment control evaluation value by processing the workstation transfer data, the stamping product size detection data and the unmanned warehouse detection data respectively; the memory is used to store the workstation transfer data, the stamping product size detection data, the unmanned warehouse detection data, the stamping product station transfer evaluation value, the stamping product inspection quality evaluation value and the storage environment control evaluation value.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Determine whether to perform station transfer optimization based on the stamping product station transfer evaluation value. Then, after the stamping product station transfer evaluation value meets the station transfer conditions, determine whether the specified stamping product meets the inspection quality conditions based on the obtained stamping product inspection quality evaluation value. Finally, perform a storage environment control evaluation to obtain the storage environment control evaluation value and determine whether to adjust the unmanned warehouse environment control parameters, thereby improving the accuracy of stamping product production monitoring, and further improving the accuracy of quality optimization analysis of notebook shells, effectively solving the problem of insufficient quality optimization analysis of notebook shells based on artificial intelligence in the prior art.

[0019] 2. The storage environment control evaluation value is obtained through the length change influence value, temperature difference-length change evaluation value, humidity difference-length change evaluation value and pressure-length change evaluation value, thereby achieving an accurate evaluation of the storage environment intelligent control situation of the specified stamping product, and further achieving the improvement of the reliability of evaluating the storage environment intelligent control situation of the specified stamping product.

[0020] 3. By judging whether the station transfer evaluation value of the stamping product meets the station transfer conditions, when the station transfer evaluation value of the stamping product meets the station transfer conditions, the station transfer optimization will not be performed, otherwise the station transfer optimization will be performed, thereby improving the station transfer quality of the specified stamping product, and then improving the station transfer efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a stamping parts quality optimization method based on artificial intelligence provided in an embodiment of the present application; Figure 2 A statistical diagram of changes in the temperature difference-length change evaluation value and the storage environment control evaluation value provided in the embodiment of the present application; Figure 3 A schematic diagram of the structure of a stamping parts quality optimization system based on artificial intelligence provided in an embodiment of the present application; Figure 4 A schematic diagram of a public version provided for an embodiment of the present application; Figure 5 A schematic diagram of a production line provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiments of the present application solve the problem of insufficient quality optimization analysis of notebook shells based on artificial intelligence in the prior art by providing a stamping part quality optimization method, system and device based on artificial intelligence. The station transfer data of a specified stamping product is obtained to obtain a stamping product station transfer evaluation value, and whether to perform station transfer optimization is determined based on the stamping product station transfer evaluation value. After the stamping product station transfer evaluation value meets the station transfer condition, a dimensional inspection quality evaluation is performed based on the obtained stamping product dimensional inspection data to obtain a stamping product inspection quality evaluation value. Subsequently, based on the stamping product inspection quality evaluation value, it is determined whether the specified stamping product meets the inspection quality condition. If the inspection quality condition is met, a storage environment control evaluation is performed based on the obtained unmanned warehouse inspection data to obtain a storage environment control evaluation value. Finally, based on the storage environment control evaluation value, it is determined whether to adjust the unmanned warehouse environment control parameters, thereby improving the accuracy of quality optimization analysis of the notebook shell.

[0023] The technical solution in the embodiment of the present application is to solve the problem that the quality optimization analysis of the notebook shell based on artificial intelligence is insufficient. The overall idea is as follows: The obtained stamping product station transfer evaluation value is used to determine whether to perform station transfer optimization. After the stamping product station transfer evaluation value meets the station transfer conditions, whether the specified stamping product meets the inspection quality conditions is determined based on the obtained stamping product inspection quality evaluation value. If it meets the inspection quality conditions, a storage environment control evaluation is performed to obtain a storage environment control evaluation value. Finally, based on the storage environment control evaluation value, it is determined whether to adjust the unmanned warehouse environment control parameters, thereby achieving the effect of improving the accuracy of quality optimization analysis of notebook shells.

[0024] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] like Figure 1As shown, it is a flow chart of the method for optimizing the quality of stamping parts based on artificial intelligence provided in an embodiment of the present application, and the method includes the following steps: S1, stamping product station transfer evaluation: obtain the stamping product station transfer evaluation value based on the acquired station transfer data of the specified stamping product, and judge whether to perform station transfer optimization based on the stamping product station transfer evaluation value. The stamping product station transfer evaluation value is used to evaluate the transfer of the specified stamping product during the station transfer process. The specified stamping product represents a laptop computer shell whose raw material is 5252 aluminum alloy. The station transfer optimization is used to improve the station transfer quality of the specified stamping product; S2, stamping product inspection quality evaluation: when the stamping product station transfer evaluation value meets the station transfer quality, the station transfer quality is evaluated. After the conditions are passed, a dimensional inspection quality assessment is performed based on the acquired stamping product dimensional inspection data to obtain a stamping product inspection quality assessment value, and the stamping product inspection quality assessment value is used to evaluate the inspection quality during the dimensional inspection of the specified stamping product; S3, storage environment control assessment: based on the stamping product inspection quality assessment value, it is determined whether the specified stamping product meets the inspection quality conditions. If it meets the inspection quality conditions, a storage environment control assessment is performed based on the acquired unmanned warehouse inspection data to obtain a storage environment control assessment value. Based on the storage environment control assessment value, it is determined whether to adjust the unmanned warehouse environment control parameters. The storage environment control assessment value is used to evaluate the intelligent control of the storage environment of the specified stamping product.

[0026] It should be added that the workstation transmission data includes the conveyor belt movement speed, the robot movement speed and the robot position coordinates; the robot position coordinates include the robot first position coordinates, the robot second position coordinates and the robot third position coordinates; the stamping product size detection data includes image resolution, specified stamping product length, specified stamping product width, specified stamping product thickness and product position coordinates; the product position coordinates include the product first position coordinates, the product second position coordinates and the product third position coordinates; the unmanned warehouse detection data includes the unmanned warehouse temperature difference, the unmanned warehouse humidity difference, the shelf pressure and the specified stamping product length change; the first position coordinate of the robot represents the horizontal coordinate of the robot end effector in the preset three-dimensional rectangular coordinate system; the second position coordinate of the robot represents the horizontal coordinate of the robot end effector in the preset three-dimensional rectangular coordinate system; The position coordinate represents the vertical coordinate of the manipulator end effector in the preset three-dimensional rectangular coordinate system; the third position coordinate of the manipulator represents the coordinate of the manipulator end effector in the preset three-dimensional rectangular coordinate system perpendicular to the horizontal axis and the vertical axis in the plane formed by the horizontal axis and the vertical axis; the image resolution represents the resolution of the image of the specified stamping product acquired by machine vision; the first position coordinate of the product represents the horizontal coordinate of the specified stamping product in the preset three-dimensional rectangular coordinate system; the second position coordinate of the product represents the vertical coordinate of the specified stamping product in the preset three-dimensional rectangular coordinate system; the third position coordinate of the product represents the coordinate of the specified stamping product in the preset three-dimensional rectangular coordinate system perpendicular to the horizontal axis and the vertical axis in the plane formed by the horizontal axis and the vertical axis in the preset three-dimensional rectangular coordinate system; the length change of the specified stamping product represents the length change of the specified stamping product during the storage process in the unmanned warehouse.

[0027] In this embodiment, there is a connection between the stamping product station transfer evaluation value, the stamping product inspection quality evaluation value and the storage environment control evaluation value. The quality of the station transfer is the basis of the inspection quality evaluation. When the stamping product station transfer evaluation value meets the station transfer conditions, the designated stamping product undergoes stamping product inspection quality evaluation. The stamping product inspection quality evaluation value is not only a direct reflection of the product quality, but also an important basis for determining whether the designated stamping product is suitable for entering the storage link. When the stamping product inspection quality evaluation value meets the inspection quality conditions, the storage environment control evaluation is performed; from station transfer to inspection quality, and then to storage environment control, the joint monitoring of the stamping product station transfer evaluation value, the stamping product inspection quality evaluation value and the storage environment control evaluation value realizes the dynamic adjustment and optimization of the designated stamping product production process; in this embodiment, the stamping product size detection data is acquired through real-time monitoring through an image processing algorithm (such as a Canny edge detection algorithm) to obtain the stamping product inspection quality evaluation value, and the storage environment control evaluation value is obtained through the unmanned warehouse detection data monitored by the intelligent sensor, thereby improving the accuracy of the quality optimization analysis of the notebook shell.

[0028] It needs to be explained that at a preset time, the speed of a preset point on the conveyor drive motor is measured by a speed sensor to obtain the conveyor movement speed; at a preset time, the speed of the manipulator is obtained by the servo driver or stepper motor controller built into the manipulator to obtain the manipulator movement speed; at a preset time, the position of the manipulator's end effector in the three-dimensional rectangular coordinate system is measured by a preset manipulator control system to obtain the manipulator position coordinates.

[0029] At the qualified preset time, the resolution information of the specified stamping product image is obtained through machine vision to obtain the image resolution, and the machine vision includes an industrial camera and an image processing algorithm; at the qualified preset time, the image processing algorithm in the machine vision (such as the Canny edge detection algorithm) is used to perform edge detection and size measurement on the specified stamping product image to obtain the specified stamping product length, the specified stamping product width, and the specified stamping product thickness; at the qualified preset time, the position of the specified stamping product in the three-dimensional rectangular coordinate system is measured by the image processing algorithm (such as the Canny edge detection algorithm and the least squares method) to obtain the product position coordinates.

[0030] The temperature and humidity of the unmanned warehouse at the initial and final states in a preset time period are monitored respectively by the built-in temperature sensor and humidity sensor of the unmanned warehouse, and the difference between the temperature and humidity at the initial and final states is obtained to obtain the temperature difference and humidity difference of the unmanned warehouse; the pressure at the preset position point of the shelf is measured by the pressure sensor to obtain the shelf pressure; the length of the specified stamping product at the initial and final states in the preset time period is measured by the electronic micrometer, and the difference between the length of the initial and final states is obtained to obtain the length change of the specified stamping product.

[0031] Furthermore, the specific process of obtaining the station transfer evaluation value of the stamping product according to the acquired station transfer data of the specified stamping product is as follows: The conveyor belt moving speed evaluation value is obtained by performing a ratio operation between the conveyor belt moving speed and the preset conveyor belt moving speed threshold obtained from the database and then performing an integration process. The limiting expression of the conveyor belt moving speed evaluation value is: , ; Indicates the current monitoring time. Indicates the end time of monitoring. represents the conveyor belt moving speed at the preset time Q, Indicates the preset conveyor belt moving speed threshold. represents the estimated value of the conveyor belt moving speed, and Q represents the preset time.

[0032] Get the robot moving speed evaluation value. The robot moving speed evaluation value is obtained by performing a ratio operation on the sum of the robot moving speed and the preset robot moving speed threshold obtained from the database and twice the preset robot moving speed threshold, and then performing an integral process. The robot moving speed evaluation value is greater than 0. The limiting expression of the robot moving speed evaluation value is: ; represents the movement speed of the robot at the preset time Q, , Indicates the current monitoring time. Indicates the end time of monitoring. Indicates the preset robot movement speed threshold. Indicates the estimated value of the robot movement speed.

[0033] The first positioning accuracy evaluation value is obtained by processing the first position coordinate of the manipulator and the first coordinate of the preset manipulator final state obtained from the database. The first positioning accuracy evaluation value is greater than 0. The restriction expression of the first positioning accuracy evaluation value is: ; represents the first position coordinate of the manipulator at the preset time Q, Indicates the first coordinate of the preset robot's final state. Indicates the first positioning accuracy evaluation value.

[0034] The second positioning accuracy evaluation value is obtained by processing the second position coordinate of the manipulator and the second coordinate of the preset manipulator final state obtained from the database. The second positioning accuracy evaluation value is greater than 0. The restriction expression of the second positioning accuracy evaluation value is: ; represents the second position coordinate of the manipulator at the preset time Q, Indicates the second coordinate of the preset robot's final state. Represents the second positioning accuracy evaluation value.

[0035] The third positioning accuracy evaluation value is obtained by processing the third position coordinate of the manipulator and the third coordinate of the preset manipulator final state obtained from the database. The third positioning accuracy evaluation value is greater than 0. The limiting expression of the third positioning accuracy evaluation value is: ; represents the third position coordinate of the manipulator at the preset time Q, Indicates the third coordinate of the preset robot's final state. Represents the third positioning accuracy evaluation value.

[0036] When the conveyor belt moving speed evaluation value is within the preset conveyor belt moving speed range obtained from the database, the stamping product station transfer evaluation value is obtained by using the obtained conveyor belt moving speed evaluation value, the robot moving speed evaluation value, the first positioning accuracy evaluation value, the second positioning accuracy evaluation value and the third positioning accuracy evaluation value; when the conveyor belt moving speed evaluation value is not within the preset conveyor belt moving speed range obtained from the database, the conveyor belt moving speed evaluation value is recorded as 0, and the stamping product station transfer evaluation value is obtained by using the obtained robot moving speed evaluation value, the first positioning accuracy evaluation value, the second positioning accuracy evaluation value and the third positioning accuracy evaluation value.

[0037] Among them, the limiting expression of the transfer evaluation value of the stamping product station is as follows: ; In the formula, Indicates the transfer evaluation value of stamping product station. It represents the estimated value of the conveyor belt moving speed. Indicates the estimated value of the robot's moving speed. represents the first positioning accuracy evaluation value, represents the second positioning accuracy evaluation value, represents the third positioning accuracy evaluation value, e represents a natural constant, Indicates the preset conveyor belt moving speed range.

[0038] In this embodiment, the aforementioned database is a database for storing various types of setting data established before the design of the artificial intelligence-based stamping part quality optimization method provided in the embodiment of the present application. The database includes but is not limited to the conveyor belt moving speed, robot moving speed, unmanned warehouse temperature difference, etc., and the various numerical values ​​therein are directly set by technical personnel; for example, the preset conveyor belt moving speed threshold is represented by the maximum value of the conveyor belt moving speed in the historical time period in the database, the preset robot moving speed threshold is represented by the maximum value of the robot moving speed in the historical time period in the database, the preset robot final state first coordinate, the preset robot final state second coordinate and the preset robot final state third coordinate are set in advance by the preset personnel, the upper limit of the preset conveyor belt moving speed range is represented by the maximum value of the conveyor belt moving speed influence value in the historical time period, and the lower limit of the preset conveyor belt moving speed range is represented by the average value of the conveyor belt moving speed influence value in the historical time period.

[0039] The algorithm of this embodiment combines the workstation transfer data analysis to obtain the stamping product workstation transfer evaluation value. The greater the robot movement speed and the conveyor belt movement speed, the faster the workstation transfer rate, resulting in an increase in the stamping product workstation transfer evaluation value; the greater the square of the difference between the robot first position coordinate, the robot second position coordinate, and the robot third position coordinate and the corresponding preset product position coordinate, the less accurate the robot positioning is, resulting in a decrease in the stamping product workstation transfer evaluation value.

[0040] In the algorithm of this embodiment, the station transfer data does not exist independently, and the variables are interrelated, which requires comprehensive analysis. If the conveyor belt speed is faster and the robot speed is slower, the robot may not be able to grab the product in time; conversely, if the robot speed is faster and the conveyor belt speed is slower, it may cause the robot to wait too long, reducing production efficiency. The increase in the moving speed of the conveyor belt and the moving speed of the robot may cause the positioning accuracy of the robot to decrease, that is, the square of the difference between the first position coordinate of the robot, the second position coordinate of the robot, and the third position coordinate of the robot and the corresponding preset product position coordinate increases, which in turn leads to a decrease in the transfer evaluation value of the stamping product station; if the positioning of the first position coordinate of the robot and the second position coordinate of the robot is inaccurate, it may cause the positioning of the third position coordinate of the robot to be inaccurate, which in turn leads to a decrease in the transfer evaluation value of the stamping product station. By analyzing the comprehensive influence between the parameters, an accurate evaluation of the transfer of the specified stamping product during the station transfer process is achieved, thereby improving the accuracy of the quality optimization analysis of the notebook shell.

[0041] Furthermore, the specific process of judging whether to perform station transfer optimization based on the stamping product station transfer evaluation value is as follows: judging whether the stamping product station transfer evaluation value meets the station transfer conditions. When the stamping product station transfer evaluation value meets the station transfer conditions, it indicates that the designated stamping product station transfer is qualified and the station transfer optimization is not performed; when the stamping product station transfer evaluation value does not meet the station transfer conditions, it indicates that the designated stamping product station transfer is unqualified and the station transfer optimization is performed; when the stamping product station transfer evaluation value still does not meet the station transfer conditions after the station transfer optimization, an alarm is sent to the preset personnel; the station transfer optimization includes manipulator preload and conveyor belt traction adjustment; manipulator preload means improving the manipulator performance by preloading; conveyor belt traction adjustment means sending a prompt to the preset personnel to adjust the conveyor belt traction by a preset multiple; the station transfer condition indicates that the stamping product station transfer evaluation value is not lower than the preset station transfer average threshold obtained from the database.

[0042] In this embodiment, the preset workstation transfer average threshold is represented by the average value of the stamping product workstation transfer evaluation value in the historical time period in the database; when it is monitored that the stamping product workstation transfer evaluation value does not meet the transfer conditions, the preset personnel is prompted to adjust the preload of the manipulator, and the preset personnel sets the preload value according to the specifications of the manipulator and the material of the specified stamping product to ensure that the performance of the manipulator reaches the optimal state; sending a prompt to the preset personnel to adjust the traction force of the conveyor belt by a preset multiple can change its transportation speed or carrying capacity, which helps to improve the efficiency and stability of the production line; thereby achieving an improvement in the accuracy of quality optimization analysis of notebook shells.

[0043] Furthermore, the specific process of obtaining the stamping product inspection quality assessment value is as follows: The resolution evaluation value is obtained by performing a ratio operation on the sum of the image resolution obtained from the database and the preset resolution threshold and then integrating the values ​​of the two times the preset resolution threshold. The resolution evaluation value is greater than 0. The restriction expression of the resolution evaluation value is: , ; Indicates the current monitoring time corresponding to the qualified preset time. The qualified preset time indicates the preset time corresponding to the station transfer condition. Indicates the monitoring end time corresponding to the qualified preset time, Indicates the image resolution at the preset time S, represents the preset resolution threshold, S represents the qualified preset time, Indicates the resolution evaluation value.

[0044] Get the length error evaluation value. The length error evaluation value is obtained by processing the specified stamping product length and the preset length threshold obtained from the database. The length error evaluation value is greater than 0. The length error evaluation value is limited by the expression: dS; Indicates the length of the specified stamping product at the preset time S, Indicates the preset length threshold, Indicates the length error estimate.

[0045] Get the width error evaluation value. The width error evaluation value is obtained by processing the specified stamping product width and the preset width threshold obtained from the database. The width error evaluation value is greater than 0. The restriction expression of the width error evaluation value is: ; Indicates the width of the specified stamping product at the preset time S, Indicates the preset width threshold, Indicates the width error estimate.

[0046] Get the thickness error evaluation value. The thickness error evaluation value is obtained by processing the specified stamping product thickness and the preset thickness threshold obtained from the database. The thickness error evaluation value is greater than 0. The restriction expression of the thickness error evaluation value is: ; Indicates the thickness of the specified stamping product at the preset time S, Indicates the preset thickness threshold, Indicates the thickness error evaluation value.

[0047] Get the product positioning accuracy evaluation value. The product positioning accuracy evaluation value is obtained by processing the product location coordinates, the preset product location coordinates obtained from the database, and the preset positioning accuracy evaluation threshold. The product positioning accuracy evaluation value is greater than 0. The restriction expression of the product positioning accuracy evaluation value is: ; Indicates the first position coordinate of the product at the preset time S, Indicates the second position coordinate of the product at the preset time S, Indicates the third position coordinate of the product at the preset time S, Indicates the first and last state position coordinates of the preset product. Indicates the coordinates of the second final state of the preset product. Indicates the third final state position coordinates of the preset product. Indicates the preset positioning accuracy assessment threshold. Indicates the product positioning accuracy evaluation value.

[0048] The stamping product inspection quality assessment value is obtained by obtaining the resolution assessment value, length error assessment value, width error assessment value, thickness error assessment value and product positioning accuracy assessment value; the preset product position coordinates include the preset product first final state position coordinates, the preset product second final state position coordinates, and the preset product third final state position coordinates.

[0049] Among them, the stamping product inspection quality assessment value is obtained by the following method: ; In the formula, Indicates the quality assessment value of stamping product inspection. represents the resolution evaluation value, Represents the length error evaluation value, Represents the width error evaluation value, represents the thickness error evaluation value, represents the product positioning accuracy evaluation value, and e represents a natural constant.

[0050] In this embodiment, the preset resolution threshold is represented by the maximum image resolution of the stamping product specified in the historical time period, the preset length threshold is represented by the average value of the length of the stamping product specified in the historical time period, the preset width threshold is represented by the average value of the width of the stamping product specified in the historical time period, and the preset thickness threshold is represented by the average value of the thickness of the stamping product specified in the historical time period. The preset product position coordinates are directly set by the preset personnel, and the preset positioning accuracy evaluation threshold is represented by the average value of the sum of the squares of the differences between the specified stamping product length, the specified stamping product width, the specified stamping product thickness of the stamping product specified in the historical time period and the corresponding preset length threshold, the preset width threshold and the preset thickness threshold, and the square root of the difference is performed.

[0051] Specifically, assuming the resolution evaluation value Fixed to 0.5, product positioning accuracy evaluation value Fixed to 0.5, length error evaluation value The range is 1-1.5, the width error evaluation value The range is 1-1.5, the thickness error evaluation value The range is 1-1.5, as shown in Table 1, which is a statistical table of changes in the stamping product inspection quality assessment values ​​provided in the embodiment of the present application: Table 1 Statistics of changes in stamping product inspection quality assessment values Length error evaluation value CDPG Width error evaluation value KDPG Thickness error evaluation value HDPG Stamping product inspection quality assessment value JCZL 1.235 1.113 1.256 0.1088 1.351 1.367 1.384 0.0991 1.417 1.461 1.473 0.0939 ... ... ... ... As can be seen from the above table, as the length error evaluation value , Width error evaluation value , thickness error evaluation value The quality assessment value of stamping product inspection is gradually increasing. The gradual decrease means that the inspection quality of stamping products is gradually declining.

[0052] The algorithm of this embodiment combines the stamping product size detection data analysis to obtain the stamping product detection quality assessment value. The larger the image resolution, the more accurate the positioning accuracy of the manipulator, resulting in an increase in the stamping product detection quality assessment value; the larger the absolute value of the difference between the specified stamping product length, the specified stamping product width, the specified stamping product thickness and the corresponding preset length threshold, preset width threshold and preset thickness threshold, the worse the quality of the specified stamping product, resulting in a decrease in the stamping product detection quality assessment value; the larger the square of the difference between the product position coordinates and the corresponding preset product position coordinates, the more inaccurate the positioning accuracy of the specified stamping product, resulting in a decrease in the stamping product detection quality assessment value.

[0053] In the algorithm of this embodiment, the dimension detection data of stamped products does not exist independently, and each variable is interrelated, which requires comprehensive analysis. The higher the image resolution, the more product details that the machine vision can capture, thereby improving the positioning accuracy of the manipulator, resulting in a decrease in the absolute value of the difference between the product position coordinates and the corresponding preset product position coordinates, which in turn leads to an increase in the stamped product inspection quality assessment value; the length, width and thickness of a specified stamped product are interrelated. If the thickness of a specified stamped product is uneven, it may cause deviations in the length and width directions of the specified stamped product, which in turn leads to a decrease in the stamped product inspection quality assessment value. By analyzing the combined influence of the parameters, an accurate assessment of the inspection quality during the dimension inspection of a specified stamped product is achieved, thereby improving the accuracy of quality optimization analysis of notebook shells based on artificial intelligence.

[0054] Furthermore, the specific process of judging whether a specified stamping product meets the inspection quality conditions based on the stamping product inspection quality evaluation value is as follows: judging whether the stamping product inspection quality evaluation value meets the inspection quality conditions, when the stamping product inspection quality evaluation value meets the inspection quality conditions, it indicates that the stamping product inspection quality is qualified, and no mold parameter optimization is performed; when the stamping product inspection quality evaluation value does not meet the inspection quality conditions, it indicates that the stamping product inspection quality is unqualified, and mold parameter optimization is performed; after the mold parameter optimization is performed, the stamping product inspection quality evaluation value still does not meet the inspection quality conditions, and an alarm is sent to the preset personnel; mold parameter optimization means sending a prompt to the preset personnel to change the side wall height, R angle, side wall angle and side wall thickness of the mold corresponding to the specified stamping product; the inspection quality condition indicates that the stamping product inspection quality evaluation value is not lower than the preset inspection quality threshold obtained from the database.

[0055] In this embodiment, the preset detection quality threshold is represented by the average value of the stamping product detection quality assessment value in the historical time period in the database; a prompt is sent to the preset personnel to change the side wall height of the corresponding mold of the specified stamping product: blanking + positioning, arc forming, 70° bending, 90° bending, side shaping, and large surface shaping. Six sets of molds can be shared by all laptop shells. For side cutting, the mold needs to be replaced according to different heights or processed by CNC; a prompt is sent to the preset personnel to change the R angle of the corresponding mold of the specified stamping product: blanking + positioning, arc forming, 70° bending, side cutting, side shaping, and large surface shaping. Six sets of molds can be shared by all laptop shells. For 90° bending, the mold needs to be interchanged according to different R angles. Adjust the negative gap or press ribs; send a prompt to the preset personnel to change the side wall angle of the corresponding mold of the specified stamping product: blanking + positioning, arc forming, 70° bending, 90° bending, and large surface shaping. Five sets of molds can be shared by all laptop shells. For side cutting, if there is a negative angle, an additional set of M5.5 side push molds is required; a prompt is sent to the preset personnel to change the side wall thickness of the corresponding molds of the specified stamping product: blanking + positioning, arc forming, 70° bending, 90° bending, side cutting, and large surface shaping. Six sets of molds can be shared for all laptop shells. Side shaping is based on adding back extrusion molds according to different thickness requirements; it helps to dynamically adjust the structural differences of the specified stamping product, thereby improving the adaptability of the specified stamping product, and thus achieving the effect of improving the accuracy of quality optimization analysis of the laptop shell.

[0056] Furthermore, the specific process of judging whether to adjust the environmental control parameters of the unmanned warehouse based on the storage environment control evaluation value is as follows: the temperature difference of the unmanned warehouse and the preset temperature difference threshold obtained from the database are processed to obtain the temperature difference-length change evaluation value, the temperature difference-length change evaluation value is greater than 0, and the restriction expression of the temperature difference-length change evaluation value is: ; It represents the temperature difference of the unmanned warehouse corresponding to the a-th preset time period, Indicates the preset temperature difference threshold, It represents the temperature difference-length change evaluation value corresponding to the a-th preset time period.

[0057] The humidity difference in the unmanned warehouse and the preset humidity difference threshold obtained from the database are processed to obtain the humidity difference-length change evaluation value, which is greater than 0. The restriction expression of the humidity difference-length change evaluation value is: ; It represents the humidity difference of the unmanned warehouse corresponding to the a-th preset time period, Indicates the preset humidity difference threshold, Represents the humidity difference-length change evaluation value corresponding to the a-th preset time period.

[0058] The pressure-length variation evaluation value is obtained by processing the shelf pressure and the shelf pressure threshold obtained from the database. The pressure-length variation evaluation value is greater than 0. The restriction expression of the pressure-length variation evaluation value is: ; Indicates the shelf pressure corresponding to the a-th preset time period, Indicates the preset shelf pressure threshold, Indicates the pressure-length change evaluation value corresponding to the a-th preset time period.

[0059] The storage environment control evaluation value is obtained through the length change impact value, the temperature difference-length change evaluation value, the humidity difference-length change evaluation value and the pressure-length change evaluation value; the length change impact value is obtained by performing a hyperbolic tangent function on the result of the ratio operation of the sum of the length change of the specified stamping product and the preset length change threshold obtained from the database and 2 times the preset length change threshold. The length change impact value is greater than 0, and the restriction expression of the length change impact value is: ; Indicates the length change of the specified stamping product corresponding to the a-th preset time period, Indicates the preset length change threshold. Indicates the length change impact value corresponding to the a-th preset time period.

[0060] Determine whether the storage environment control evaluation value is not lower than the preset storage environment control threshold obtained from the database. When the storage environment control evaluation value is not lower than the preset storage environment control threshold, it indicates that the storage environment control is qualified and no adjustment is made to the unmanned warehouse environment control parameters. When the storage environment control evaluation value is lower than the preset storage environment control threshold, it indicates that the storage environment control is unqualified and a prompt is sent to the preset personnel to adjust the unmanned warehouse environment control parameters.

[0061] The storage environment control evaluation value is obtained by the following method: ; In the formula, represents the storage environment control evaluation value corresponding to the a-th preset time period, , a represents the number of the preset time period, b represents the total number of preset time periods, and the preset time period represents the time period that meets the detection quality conditions, represents the temperature difference-length change evaluation value corresponding to the a-th preset time period, represents the humidity difference-length change evaluation value corresponding to the a-th preset time period, represents the pressure-length variation evaluation value corresponding to the a-th preset time period, It represents the influence value of the length change corresponding to the a-th preset time period, and e represents a natural constant.

[0062] In this embodiment, the preset length change threshold is represented by the maximum value of the length change of the specified stamping product in the historical time period, the preset temperature difference threshold is represented by the maximum value of the temperature difference of the unmanned warehouse in the historical time period, the preset humidity difference threshold is represented by the maximum value of the humidity difference of the unmanned warehouse in the historical time period, the preset shelf pressure threshold is represented by the maximum value of the shelf pressure of the unmanned warehouse in the historical time period, and the preset storage environment control threshold is represented by the average value of the storage environment control evaluation value in the historical time period in the database. A prompt is sent to the preset personnel to adjust the unmanned warehouse environment control parameters, and the unmanned warehouse environment control parameters include temperature, humidity and shelf pressure; the preset personnel adjust the unmanned warehouse environment control parameters.

[0063] Specifically, assume that the length change affects the value Fixed to 0.65, humidity difference - length change evaluation value Fixed to 0.75, pressure-length variation evaluation value Fixed to 1.2, temperature difference-length change evaluation value The range is 0.35-0.75, such as Figure 2 As shown, it is a statistical diagram of the change of the temperature difference-length change evaluation value and the storage environment control evaluation value provided by the embodiment of the present application, Figure 2 It can be seen that as the temperature difference-length change evaluation value gradually increases, the storage environment control evaluation value gradually decreases, which means that the control quality of the storage environment gradually decreases.

[0064] The algorithm of this embodiment combines the unmanned warehouse detection data analysis to obtain the storage environment control evaluation value. The greater the temperature difference, humidity difference and shelf pressure of the unmanned warehouse, the greater the impact on the deformation of the specified stamping product, resulting in a decrease in the storage environment control evaluation value; the greater the change in the length of the specified stamping product, the worse the storage environment control corresponding to the specified stamping product, resulting in a decrease in the storage environment control evaluation value.

[0065] The unmanned warehouse detection data in the algorithm of this embodiment does not exist independently, and each variable is interrelated, which requires comprehensive analysis. The temperature difference and humidity difference in the unmanned warehouse may jointly affect the deformation of the product; the shelf pressure may aggravate the impact of the temperature difference and humidity difference in the unmanned warehouse on the product; the change in temperature will affect the saturated humidity of the air, thereby affecting the relative humidity in the warehouse, which may cause the specified stamping product to expand and contract, causing the length change of the specified stamping product to increase, thereby affecting its stability on the shelf and the pressure distribution on the shelf, resulting in a decrease in the storage environment control evaluation value. By analyzing the comprehensive impact between the parameters, an accurate evaluation of the intelligent control of the storage environment of the specified stamping product in the unmanned warehouse is achieved, thereby achieving an improvement in the accuracy of the quality optimization analysis of the notebook shell.

[0066] like Figure 3 As shown, it is a structural schematic diagram of the stamping parts quality optimization system based on artificial intelligence provided in the embodiment of the present application. The stamping parts quality optimization system based on artificial intelligence provided in the embodiment of the present application includes a stamping product station transfer evaluation module, a stamping product detection quality evaluation module and a storage environment control evaluation module: wherein the stamping product station transfer evaluation module is used to obtain a stamping product station transfer evaluation value according to the acquired station transfer data of a specified stamping product, and judge whether to perform station transfer optimization based on the stamping product station transfer evaluation value, and the stamping product station transfer evaluation value is used to evaluate the transfer of the specified stamping product in the station transfer process, and the specified stamping product represents a laptop computer shell whose raw material is 5252 aluminum alloy, and the station transfer optimization is used to improve the station transfer quality of the specified stamping product; The stamping product inspection quality assessment module is used to obtain a stamping product inspection quality assessment value by performing a dimensional inspection quality assessment based on the acquired stamping product dimensional inspection data after the stamping product station transfer assessment value meets the station transfer conditions. The stamping product inspection quality assessment value is used to assess the inspection quality during dimensional inspection of the specified stamping product. The storage environment control assessment module is used to determine whether the specified stamping product meets the inspection quality conditions based on the stamping product inspection quality assessment value. If the specified stamping product meets the inspection quality conditions, a storage environment control assessment is performed based on the acquired unmanned warehouse inspection data to obtain a storage environment control assessment value. Based on the storage environment control assessment value, it is determined whether to adjust the unmanned warehouse environment control parameters. The storage environment control assessment value is used to assess the intelligent control of the storage environment of the specified stamping product.

[0067] An embodiment of the present application provides an apparatus for applying a stamping part quality optimization method based on artificial intelligence, characterized in that it includes stamping part quality optimization equipment, a processor and a memory; the stamping part quality optimization equipment includes a stamping machine, a material rack, a four-axis robot, a pallet and an intelligent sensor; the intelligent sensor includes a speed sensor, a temperature sensor, a humidity sensor and a pressure sensor; the processor is used to obtain a stamping product station transfer evaluation value, a stamping product inspection quality evaluation value and a storage environment control evaluation value by processing the workstation transfer data, the stamping product size detection data and the unmanned warehouse detection data respectively; the memory is used to store the workstation transfer data, the stamping product size detection data, the unmanned warehouse detection data, the stamping product station transfer evaluation value, the stamping product inspection quality evaluation value and the storage environment control evaluation value.

[0068] In this embodiment, the stamping product station transfer evaluation module obtains the stamping product station transfer evaluation value based on the station transfer data. If the evaluation value does not meet the station transfer conditions, the station transfer optimization is required; if the evaluation value meets the station transfer conditions, the stamping product inspection quality evaluation module obtains the stamping product inspection quality evaluation value based on the size inspection data. If the evaluation value does not meet the inspection quality conditions, the mold parameter optimization is performed; if the evaluation value meets the inspection quality conditions, the storage environment control evaluation module obtains the output storage environment control evaluation value based on the unmanned warehouse inspection data. When the storage environment control evaluation value is lower than the preset storage environment control threshold, the unmanned warehouse environment control parameters are adjusted; the stamping product station transfer evaluation module, the stamping product inspection quality evaluation module and the storage environment control evaluation module interact with each other, which helps to ensure the quality of stamping parts from production to storage, thereby achieving the effect of improving the accuracy of quality optimization analysis of notebook shells.

[0069] like Figure 5 As shown in the figure, it is a schematic diagram of the production line provided in the embodiment of the present application. As can be seen from the figure, the production line provides a stamping part quality optimization device; the specified stamping product provided in the embodiment of the present application is a laptop computer shell whose raw material is 5252 aluminum alloy, and the titanium content in 5252 aluminum alloy is greater than that in 5052 aluminum alloy. Due to the grain refinement, the later anodizing performance of 5252 aluminum alloy is better than that of 5052, so 5252 aluminum alloy is selected as the raw material.

[0070] To summarize, the embodiment of the present application determines whether to perform station transfer optimization through the stamping product station transfer evaluation value, and then, after the stamping product station transfer evaluation value meets the station transfer conditions, determines whether the specified stamping product meets the inspection quality conditions based on the acquired stamping product inspection quality evaluation value, and finally performs a storage environment control evaluation to obtain the storage environment control evaluation value and determine whether to adjust the unmanned warehouse environment control parameters, thereby achieving an improvement in the accuracy of stamping product production monitoring, and further achieving an improvement in the accuracy of quality optimization analysis of notebook shells, effectively solving the problem of insufficient quality optimization analysis of notebook shells based on artificial intelligence in the prior art.

[0071] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. The method for optimizing stamping parts quality based on artificial intelligence is characterized by: The following steps are involved: S1, obtaining a station transfer evaluation value of a stamping product according to the acquired station transfer data of a specified stamping product, and judging whether to perform station transfer optimization based on the station transfer evaluation value of the stamping product, wherein the station transfer evaluation value of the stamping product is used to evaluate the transfer condition of the specified stamping product during the station transfer process, and the station transfer optimization is used to improve the station transfer quality of the specified stamping product; S2, after the stamping product station transfer evaluation value meets the station transfer conditions, a dimensional inspection quality evaluation is performed based on the acquired stamping product dimensional inspection data to obtain a stamping product inspection quality evaluation value, which is used to evaluate the inspection quality during the dimensional inspection of the designated stamping product; S3, based on the stamping product inspection quality evaluation value, determine whether the specified stamping product meets the inspection quality conditions. If it meets the inspection quality conditions, perform storage environment control evaluation based on the acquired unmanned warehouse inspection data to obtain a storage environment control evaluation value. Based on the storage environment control evaluation value, determine whether to adjust the unmanned warehouse environment control parameters. The storage environment control evaluation value is used to evaluate the intelligent control of the storage environment of the specified stamping product.

2. The method for optimizing stamping parts quality based on artificial intelligence as claimed in claim 1, characterized in that: The workstation transfer data includes the conveyor belt moving speed, the robot moving speed and the robot position coordinates; The manipulator position coordinates include the manipulator first position coordinates, the manipulator second position coordinates and the manipulator third position coordinates; The stamping product size detection data includes image resolution, designated stamping product length, designated stamping product width, designated stamping product thickness and product position coordinates; The product position coordinates include the product first position coordinates, the product second position coordinates, and the product third position coordinates; The unmanned warehouse detection data includes the unmanned warehouse temperature difference, the unmanned warehouse humidity difference, the shelf pressure and the length change of the specified stamping product; The first position coordinate of the manipulator represents the horizontal coordinate of the end effector of the manipulator in a preset three-dimensional rectangular coordinate system; The second position coordinate of the manipulator represents the ordinate of the end effector of the manipulator in a preset three-dimensional rectangular coordinate system; The third position coordinate of the manipulator represents the coordinate of the manipulator end effector in a plane formed by a preset three-dimensional rectangular coordinate system perpendicular to the horizontal axis and the vertical axis; The first position coordinate of the product represents the horizontal coordinate of the designated stamping product in a preset three-dimensional rectangular coordinate system; The second position coordinate of the product represents the ordinate of the designated stamping product in a preset three-dimensional rectangular coordinate system; The third position coordinate of the product represents the coordinate of a specified stamping product in a plane formed by a preset three-dimensional rectangular coordinate system perpendicular to the horizontal axis and the vertical axis.

3. The method for optimizing stamping parts quality based on artificial intelligence as claimed in claim 2, characterized in that: The specific process of obtaining the station transfer evaluation value of a stamping product according to the acquired station transfer data of a designated stamping product is as follows: The conveyor belt moving speed evaluation value is obtained by performing a ratio operation between the conveyor belt moving speed and a preset conveyor belt moving speed threshold obtained from a database and then performing an integration process; Obtaining a manipulator movement speed evaluation value, wherein the manipulator movement speed evaluation value is obtained by performing a ratio operation on the sum of the manipulator movement speed and a preset manipulator movement speed threshold obtained from a database and twice the preset manipulator movement speed threshold, and then performing an integration process; A first positioning accuracy evaluation value is obtained by processing the first position coordinates of the manipulator and the first coordinates of the preset final state of the manipulator obtained from the database; The second positioning accuracy evaluation value is obtained by processing the second position coordinates of the manipulator and the second coordinates of the preset final state of the manipulator obtained from the database; A third positioning accuracy evaluation value is obtained by processing the third position coordinate of the manipulator and the third coordinate of the preset manipulator final state obtained from the database; When the conveyor belt moving speed evaluation value is within the preset conveyor belt moving speed range obtained from the database, the stamping product station transfer evaluation value is obtained by obtaining the conveyor belt moving speed evaluation value, the robot moving speed evaluation value, the first positioning accuracy evaluation value, the second positioning accuracy evaluation value and the third positioning accuracy evaluation value; When the conveyor belt moving speed evaluation value is not within the preset conveyor belt moving speed range obtained from the database, the conveyor belt moving speed evaluation value is recorded as 0, and the stamping product station transfer evaluation value is obtained by the obtained manipulator moving speed evaluation value, the first positioning accuracy evaluation value, the second positioning accuracy evaluation value and the third positioning accuracy evaluation value.

4. The method for optimizing stamping quality based on artificial intelligence as claimed in claim 3, characterized in that: The limiting expression of the transfer evaluation value of the stamping product station is as follows: ； In the formula, Indicates the transfer evaluation value of stamping product station. It represents the estimated value of the conveyor belt moving speed. Indicates the estimated value of the robot's moving speed. represents the first positioning accuracy evaluation value, represents the second positioning accuracy evaluation value, represents the third positioning accuracy evaluation value, e represents a natural constant, Indicates the preset conveyor belt moving speed range.

5. The method for optimizing stamping parts quality based on artificial intelligence as claimed in claim 4, characterized in that: The specific process of judging whether to perform station transfer optimization based on the station transfer evaluation value of the stamping product is as follows: When the stamping product station transfer evaluation value meets the station transfer conditions, it indicates that the designated stamping product station transfer is qualified and station transfer optimization is not performed; When the stamping product station transfer evaluation value does not meet the station transfer conditions, it indicates that the designated stamping product station transfer is unqualified, and the station transfer optimization is performed; When the station transfer evaluation value of the stamping product still does not meet the station transfer conditions after the station transfer optimization, an alarm prompt is sent to the preset personnel; The workstation transfer optimization includes robot preload and conveyor belt traction adjustment; The workstation transfer condition indicates that the workstation transfer evaluation value of the stamping product is not lower than a preset workstation transfer average threshold obtained from the database.

6. The method for optimizing stamping parts quality based on artificial intelligence as claimed in claim 2, characterized in that: The specific process of obtaining the stamping product inspection quality assessment value is as follows: Obtaining a resolution evaluation value, wherein the resolution evaluation value is obtained by performing a ratio operation on the sum of the image resolution obtained from the database and a preset resolution threshold and twice the preset resolution threshold, and then performing an integration process; Obtaining a length error evaluation value, wherein the length error evaluation value is obtained by processing a specified stamping product length and a preset length threshold obtained from a database; Obtaining a width error evaluation value, wherein the width error evaluation value is obtained by processing a specified stamping product width and a preset width threshold obtained from a database; Obtaining a thickness error evaluation value, wherein the thickness error evaluation value is obtained by processing a specified stamping product thickness and a preset thickness threshold obtained from a database; Obtaining a product positioning accuracy evaluation value, wherein the product positioning accuracy evaluation value is obtained by processing the product position coordinates and the preset product position coordinates and the preset positioning accuracy evaluation threshold obtained from the database; The stamping product inspection quality assessment value is obtained by obtaining the resolution assessment value, length error assessment value, width error assessment value, thickness error assessment value and product positioning accuracy assessment value; The preset product position coordinates include the preset product first final state position coordinates, the preset product second final state position coordinates, and the preset product third final state position coordinates.

7. The method for optimizing stamping parts quality based on artificial intelligence as claimed in claim 1, characterized in that: The specific process of judging whether a specified stamping product meets the inspection quality conditions based on the stamping product inspection quality evaluation value is as follows: When the stamping product inspection quality assessment value meets the inspection quality conditions, it indicates that the stamping product inspection quality is qualified and the mold parameter optimization is not performed; When the stamping product inspection quality assessment value does not meet the inspection quality conditions, it indicates that the stamping product inspection quality is unqualified, and the mold parameters are optimized; When the stamping product inspection quality assessment value still does not meet the inspection quality conditions after the mold parameters are optimized, an alarm prompt is sent to the preset personnel; The mold parameter optimization means sending a prompt to the preset personnel to change the side wall height, R angle, side wall angle and side wall thickness of the mold corresponding to the specified stamping product; The inspection quality condition indicates that the inspection quality evaluation value of the stamping product is not lower than a preset inspection quality threshold obtained from a database.

8. The method for optimizing stamping quality based on artificial intelligence as claimed in claim 2, characterized in that: The specific process of judging whether to adjust the unmanned warehouse environment control parameters based on the storage environment control evaluation value is as follows: The temperature difference of the unmanned warehouse and the preset temperature difference threshold obtained from the database are processed to obtain the temperature difference-length change evaluation value; The humidity difference of the unmanned warehouse and the preset humidity difference threshold obtained from the database are processed to obtain a humidity difference-length change evaluation value; The pressure-length variation evaluation value is obtained by processing the shelf pressure and the shelf pressure threshold value obtained from the database; The storage environment control evaluation value is obtained through the length change impact value, the temperature difference-length change evaluation value, the humidity difference-length change evaluation value and the pressure-length change evaluation value; The length variation influence value is obtained by performing a hyperbolic tangent function on the result of a ratio operation between the sum of the length variation of the specified stamping product and a preset length variation threshold obtained from a database and twice the preset length variation threshold; Determine whether the storage environment control evaluation value is not lower than the preset storage environment control threshold obtained from the database. When the storage environment control evaluation value is not lower than the preset storage environment control threshold, it indicates that the storage environment control is qualified, and no adjustment of the unmanned warehouse environment control parameters is performed; When the storage environment control evaluation value is lower than the preset storage environment control threshold, it indicates that the storage environment control is unqualified, and a prompt is sent to the preset personnel to adjust the unmanned warehouse environment control parameters.

9. The stamping parts quality optimization system based on artificial intelligence is characterized by: It includes stamping product station transfer assessment module, stamping product inspection quality assessment module and storage environment control assessment module: Among them, the stamping product station transfer evaluation module is used to obtain a stamping product station transfer evaluation value based on the acquired station transfer data of the specified stamping product, and judge whether to perform station transfer optimization based on the stamping product station transfer evaluation value. The stamping product station transfer evaluation value is used to evaluate the transfer of the specified stamping product during the station transfer process, and the station transfer optimization is used to improve the station transfer quality of the specified stamping product; The stamping product inspection quality assessment module is used to obtain a stamping product inspection quality assessment value by performing a dimensional inspection quality assessment based on the acquired stamping product dimensional inspection data after the stamping product station transfer assessment value meets the station transfer conditions. The stamping product inspection quality assessment value is used to assess the inspection quality during the dimensional inspection of a specified stamping product. The storage environment control evaluation module is used to determine whether a specified stamping product meets the inspection quality conditions based on the stamping product inspection quality evaluation value. If it meets the inspection quality conditions, a storage environment control evaluation is performed based on the acquired unmanned warehouse inspection data to obtain a storage environment control evaluation value. Based on the storage environment control evaluation value, it is determined whether to adjust the unmanned warehouse environment control parameters. The storage environment control evaluation value is used to evaluate the intelligent control of the storage environment of the specified stamping product.

10. A device for applying the method for optimizing stamping quality based on artificial intelligence as described in any one of claims 1 to 8, characterized in that: Includes stamping parts quality optimization equipment, processor and memory; The stamping parts quality optimization equipment includes a stamping machine, a material rack, a four-axis robot, a pallet and an intelligent sensor; The intelligent sensors include a speed sensor, a temperature sensor, a humidity sensor and a pressure sensor; The processor is used to obtain a stamping product station transfer evaluation value, a stamping product detection quality evaluation value and a storage environment control evaluation value by processing the station transfer data, the stamping product size detection data and the unmanned warehouse detection data respectively; The memory is used to store workstation transfer data, stamping product size detection data, unmanned warehouse detection data, stamping product workstation transfer evaluation values, stamping product detection quality evaluation values ​​and storage environment control evaluation values.

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