Intelligent control system and control method for production line

Abstract

The invention discloses an intelligent control system and method for a production line, and relates to the technical field of intelligent manufacturing, and the system comprises a first monitoring device which is used for detecting whether the size of a mounting hole of a bearing cover turned by a numerical control lathe and the flatness of the front end face are qualified or not, and is also used for monitoring the spindle rotating speed and the cutter feeding speed of the numerical control lathe; the conveying assembly is used for conveying the blanks to be machined and the detected workpieces to the intelligent storage unit or the machining position; the second monitoring device is used for detecting whether the size of the connecting hole machined by the machining center is qualified or not; the system control device is used for receiving detection signals sent by the first monitoring device and the second monitoring device and generating control instructions of the numerical control lathe, the machining center and the conveying assembly. The manufacturing process of the bearing caps can be monitored and intelligently analyzed, the manufactured bearing caps are automatically detected and classified, and the production efficiency of the bearing caps is improved. Automatic production control is achieved, and meanwhile production efficiency and quality are improved.

Description

Technical Field

[0001] This application relates to the field of intelligent manufacturing technology, and particularly relates to an intelligent control system and a control method for a production line. Background Art

[0002] The bearing cover of a motor is mainly used to protect the bearing. The bearing cover plays a sealing role, which can prevent dust from entering the bearing interior and causing damage, thereby reducing the usage risk. Although the existing bearing cover processing system uses automation to replace manual operation, it is not easy to make mistakes and reduces the labor intensity of workers. However, during the processing of the bearing cover, abnormal failures of the machine will also affect the product quality, resulting in potential safety hazards during installation and use.

[0003] To ensure that it can be correctly installed on the motor and play its due role, it is necessary to strictly control its production quality. The traditional method for detecting the production quality of bearing covers generally involves disassembling the parts after each process is completed and using manual operation instruments for quality monitoring. This method cannot timely detect quality problems and handle machine failures during the operation of the production line, thereby reducing production losses, and cannot analyze and optimize production quality problems from multiple aspects. Therefore, it is necessary to optimize the existing production method. Summary of the Invention

[0004] In view of this, this application provides an intelligent control system and a control method for a production line, which solves the technical problems that the traditional method for detecting the quality of motor bearing covers generally conducts quality monitoring after production is completed, cannot timely detect quality problems and handle machine failures during the operation of the production line, thereby reducing production losses, cannot analyze and optimize production quality problems from multiple aspects, lacks detection efficiency when detecting quality performance, and may damage the surface of the parts.

[0005] To achieve the above object, the present invention provides the following technical solutions: An intelligent control system for a production line, comprising: a first monitoring device, a transportation component, a second monitoring device, and a system control device; The first monitoring device is used to detect whether the installation hole size and the flatness of the front end face of the bearing cover turned by a numerical control lathe are qualified, and is also used to monitor the spindle speed and the tool feed speed of the numerical control lathe; The transportation component is used to transport the blank to be processed and the workpiece after inspection to the intelligent storage unit or the processing position; The second monitoring device is used to detect whether the size of the connection hole processed by a machining center is qualified, and is also used to monitor the rotational speed information of the processing spindle of the machining center; The system control device is used to receive the first detection signal sent by the first monitoring device and the second detection signal sent by the second monitoring device, and generate control instructions for the numerically controlled lathe, machining center, and transportation component.

[0006] Further, the first monitoring device includes a sensing module, a timing module, and a first sub-control module deployed on the numerically controlled lathe; The sensing module includes a laser measurement device, a magnetic induction encoder, and a displacement sensor. The laser measurement device includes a bracket and a line laser scanner disposed at the top of the bracket. The line laser scanner is used to collect three-dimensional point cloud data of the front end face of the bearing cover. The magnetic induction encoder is used to detect the rotational speed information of the main shaft. The displacement sensor is used to detect the feed displacement information of the tool on the numerically controlled lathe. The timing module is used to record the working time parameters of the machine tool.

[0007] Generally, a hydraulic chuck for clamping the part to be machined and a tool rest disposed opposite to the hydraulic chuck are mainly provided on the bed of the numerically controlled lathe. The bracket is a robotic arm installed on the numerically controlled lathe. Taking the plane parallel to the front end face of the bearing cover as the X-Y plane and the plane perpendicular to the front end face of the bearing cover as the Z-Y plane to establish a coordinate system, the robotic arm can move longitudinally along the Y axis, thereby driving the line laser scanner at the top to move and completing the scanning action of the front end face of the bearing cover. The magnetic induction encoder is disposed at the motor bearing end cover of the main shaft motor and can directly monitor the rotational speed information of the main shaft. The displacement sensor uses a laser displacement sensor. The laser displacement sensor is disposed on the bed. The laser displacement sensor emits a light beam to the tool and receives the reflected signal. According to the change of the reflected signal, the feed position and speed of the tool can be obtained.

[0008] The first sub-control module includes a flatness analysis module, a dimension analysis module, a parameter analysis module, and an information transmission module. The flatness analysis module is used to optimize the acquired three-dimensional point cloud data through a filtering algorithm, extract the front-end point cloud data of the bearing cover, segment the point cloud, traverse each segment of the point cloud, streamline the point cloud data, and fit the point cloud plane using the least squares method. Finally, traverse each point to obtain the point with the maximum distance from the fitted plane, and obtain the flatness value. Compare the obtained flatness value with a preset feature threshold to determine whether the flatness is qualified. If it is qualified, output a qualified signal; otherwise, output an unqualified signal. The dimension analysis module is used to traverse the front-end point cloud data of the bearing cover obtained, streamline the point cloud data using the voxel grid method, and perform point cloud filtering based on the SOR filtering algorithm to obtain the edge feature information of the holes in the point cloud model. Fit the edge data using the least squares method and determine the feature parameters of the holes. The feature parameters of the holes include the diameter of the fitted circle. Perform hole feature measurement multiple times and take the average value as the reference value. Compare the reference value with a preset threshold to determine whether the installation hole size is qualified. If it is qualified, output a qualified signal; otherwise, output an unqualified signal. The parameter analysis module is used to obtain the spindle speed information, compare the spindle speed information with a preset threshold to determine whether there is an abnormality. If there is an abnormality, output an abnormality alarm signal. It is also used to calculate the tool feed speed per unit time based on the displacement information, compare the tool feed speed with a preset threshold to determine whether there is an abnormality. If there is an abnormality, output an abnormality alarm signal. The information transmission module is used to send detection signals to the system control device.

[0009] After acquiring the three-dimensional point cloud data, first use a filtering algorithm to remove noise points and smooth the data to improve the accuracy of subsequent analysis. Then, from the entire three-dimensional point cloud data, identify and extract the point cloud data related to the front end face of the bearing cover for subsequent calculations.

[0010] Furthermore, the transportation component includes an AGV cart component and a robotic arm component. The AGV cart component includes an AGV cart and an AGV controller installed on the AGV cart. The AGV controller is used to control the AGV cart to perform corresponding transportation work according to the transportation instructions sent by the system control device, and during transportation, use an obstacle avoidance algorithm based on deep learning for obstacle avoidance transportation. The robotic arm component includes a six-axis robotic arm, a track, and a robot controller installed on the six-axis robotic arm. The six-axis robotic arm is slidably installed on the track. The robot controller is used to control the six-axis robotic arm to complete the transfer and installation operations of the blank and the workpiece according to the transportation instructions sent by the system control device.

[0011] Furthermore, the second monitoring device includes a vision detection module, a Hall sensor, and a second sub-control module deployed in the machining center. The vision detection module includes an industrial camera, a light source module, and an image acquisition device. The industrial camera is used to capture images of the front end face of the drilled part. The light source module is used to supplement light for the industrial camera. The light source module includes a ring-shaped LED lamp group and an LED dimming circuit. The ring-shaped LED lamp group is connected to the image acquisition device through the LED dimming circuit. The LED dimming circuit is used to adaptively adjust the brightness of the LED lights according to the control signal output by the image acquisition device. Both the industrial camera and the light source module are connected to the image acquisition device. The image acquisition device is used to receive the analog image signal output by the industrial camera, convert it into a digital image signal through an A / D converter, control the photographing parameters of the industrial camera through an input / output interface, and store the digital image data; The second sub-control module includes an image processing module, a feature extraction module, a classification output module, and a comparison and analysis module. The image processing module obtains the digital image data and sequentially performs image gray-scale transformation processing, mean filtering processing, and binarization processing on the image data to obtain a segmented binary image. The feature extraction module uses an edge detection algorithm based on the Canny operator to perform edge detection on the preprocessed image, then performs circle fitting by the least squares method. According to the circle fitting result, the diameter of the circle is calculated using the rectangular coordinate system method, and the aperture information of the connection hole 11 of the bearing cover 10 is obtained according to the formula r = p / q, where r is the conversion coefficient, p is the actual aperture measurement size, and q is the pixel distance. The classification output module is used to number each connection hole, compare the calculated aperture information of each connection hole with a preset standard value, determine whether the aperture of the connection hole is within the error range, classify each connection hole according to the judgment result, and output the corresponding number and classification information of each connection hole to the system control device; The Hall sensor is used to detect the rotational speed information of the machining spindle of the machining center. The Hall sensor inputs the detection information into the comparison and analysis module. The comparison and analysis module compares the rotational speed information of the machining spindle with a preset threshold value to determine whether there is an abnormality. If there is an abnormality, an abnormal alarm signal is output.

[0012] Furthermore, the system control device includes a system controller, a data communication module, a database, and an interactive display module; The system controller includes a logic execution module and a production optimization module. The logic execution module is used to call the corresponding task execution logic according to the user interaction instruction information, call data to execute tasks according to the task execution logic, and is also used to generate control instructions for the CNC lathe, machining center, and transportation components. The production optimization module includes a safety assessment module and an optimization analysis module. The safety assessment module is used to obtain the index data of production assessment, update and manage the index data, and use the hierarchical entropy analysis method to score the production process based on the obtained index data and the index system constructed according to the first-level and second-level indexes corresponding to production events. The optimization analysis module is used to classify the production process according to the periodic change data of each optimization parameter, and match the production optimization suggestions in the expert solution library according to the optimization classification results. The data communication module is used to provide a data communication link. The data communication module is electrically connected to the system controller. The database is used to store system parameters and the monitored data collected and processed. The interactive display module is electrically connected to the system controller. The interactive display module is used to receive the user's interaction instruction information and visually display the execution result and the system working parameters.

[0013] This application also provides an intelligent control method for a production line, which is applied to the above-mentioned intelligent control system for a production line, and includes: Step S1: The system control device obtains the system startup trigger signal, initializes the system control parameters, and sends a control instruction to the AGV controller. The AGV controller controls the AGV trolley to transport the blank from the blank storage unit to the transfer location and sends feedback information to the system control device. The system control device sends a transportation instruction to the robot controller. The robot controller controls the six-axis robotic arm to complete the transfer and installation operations of the blank and sends operation feedback information to the system control device. Step S2: The system control device sends a control instruction to the CNC lathe. The CNC lathe turns the front end face and the mounting hole of the bearing cover according to the preset operation process, and at the same time controls the first monitoring device to monitor the spindle speed and the tool feed speed of the CNC lathe. After the operation is completed, the system control device controls the first monitoring device to detect whether the size of the mounting hole and the flatness of the front end face of the bearing cover turned by the CNC lathe are qualified. Step S3: The system control device sends a signal to the robot controller according to the classification result. The robot controller controls the six-axis robotic arm to complete the transfer and installation operations of the workpiece, and sends a control signal to the AGV controller. The AGV controller controls the AGV trolley to cooperate to transport the unqualified products to the unqualified product storage unit. Step S4: The system control device sends a control instruction to the machining center. The machining center drills the connecting holes of the bearing cover according to the preset operation process. At the same time, it controls the second monitoring device to monitor the rotational speed of the machining spindle of the machining center, and after the operation is completed, the system control device controls the second monitoring device to detect whether the dimensions of the connecting holes of the bearing cover are qualified; Step S5: The system control device sends a signal to the robot controller according to the classification result fed back by the second monitoring device. The robot controller controls the six-axis robotic arm to complete the transfer operation of the drilled parts. At the same time, it sends a control signal to the AGV controller, and the AGV controller controls the AGV cart to cooperate to transport the qualified or unqualified products to the intelligent storage unit for classified placement.

[0014] Further, the first monitoring device detecting whether the installation hole dimensions and the flatness of the front end face of the bearing cover turned on the CNC lathe are qualified includes: Using a pass-through filtering method to optimize the point cloud data of the acquired three-dimensional point cloud data, extracting the front end point cloud data of the bearing cover, segmenting the point cloud, traversing each segment of the point cloud, removing outliers, thinning the point cloud data, and fitting the point cloud plane using the least squares method; Finally, traverse each point to obtain the point with the maximum distance from the fitting plane, take the distance between this point and the fitting plane as the flatness value, and compare the obtained flatness value with the preset characteristic threshold to judge whether the flatness is qualified. If it is qualified, output a qualified signal, otherwise output an unqualified signal; Traverse the front end point cloud data of the acquired bearing cover, thin the point cloud data using the voxel grid method, and perform point cloud filtering based on the SOR filtering algorithm to obtain the edge feature information of the holes in the point cloud model; Perform fitting on the edge data using the least squares method and determine the characteristic parameters of the holes. The characteristic parameters of the holes include the diameter of the fitted circle. Perform hole characteristic measurement multiple times and take the average value as the reference value. Compare the reference value with the preset threshold to judge whether the installation hole dimensions are qualified. If it is qualified, output a qualified signal, otherwise output an unqualified signal.

[0015] Further, the second monitoring device detecting whether the dimensions of the connecting holes of the bearing cover are qualified includes: Obtain digital image data, and sequentially perform image gray-scale transformation processing, mean filtering processing, and binarization processing on the image data to obtain a segmented binary image; Use an edge detection algorithm based on the Canny operator to perform edge detection on the preprocessed image, then perform circle fitting using the least squares method. According to the circle fitting result, calculate the diameter of the circle using the rectangular coordinate system method, and obtain the aperture information of the connecting hole 11 of the bearing cover 10 according to the formula r = p / q, where r is the conversion coefficient, p is the actual aperture measurement size, and q is the pixel distance; Number each connection hole, compare the calculated aperture information of each connection hole with a preset standard value to determine whether the aperture of the connection hole is within the error range, classify each connection hole according to the judgment result, and output the corresponding number and classification information of each connection hole to the system control device.

[0016] As can be seen from the above technical solutions, the advantages of the present invention are: In this application, the manufacturing process of the bearing cover can be monitored and intelligently analyzed, and the manufactured bearing covers can be automatically detected and classified. In this way, quality problems can be detected in a timely manner during the operation of the production line, reducing production losses. At the same time, automated production control is achieved, improving production efficiency and quality. Moreover, the production quality and safety can be analyzed and optimized from multiple aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application.

[0018] Figure 1 It is a schematic structural diagram of the composition of this application.

[0019] Figure 2 It is a schematic layout diagram of the equipment of this application.

[0020] Figure 3 It is a three-dimensional structural diagram of the blank of the bearing cover.

[0021] Figure 4 It is another three-dimensional diagram of the blank of the bearing cover.

[0022] Figure 5 It is a three-dimensional diagram of the bearing cover.

[0023] Figure 6 It is another three-dimensional diagram of the bearing cover.

[0024] Figure 7 It is a schematic diagram of the processing flow of the numerical control lathe of this application.

[0025] Figure 8 It is a schematic diagram of the processing flow of the machining center of this application.

[0026] Figure 9 It is a schematic diagram of the steps of the intelligent control method for the production line of this application.

[0027] Figure 10 It is a top view of the numerical control lathe of this application.

[0028] Reference Signs: Blank - 100, bearing cover - 10, connection hole - 11, mounting hole - 12, front end face - 13, track - 20, CNC lathe - 30, machining center - 40, six - axis robotic arm - 50, AGV cart - 60, intelligent storage unit - 70, blank storage unit - 71, qualified product storage unit - 72, unqualified product storage unit - 73, system control device - 80, hydraulic chuck - 200, tool rest - 300, robotic arm - 400. Detailed implementation manners

[0029] To make the objectives, technical solutions and advantages of the present application more clearly understood, the following further elaborates on the present application in conjunction with the implementation manners and the accompanying drawings. Herein, the illustrative implementation manners of the present application and their descriptions are used to explain the present application, but are not intended to limit the present application.

[0030] Refer to Figures 1 to 10 , Figure 3 is a three - dimensional structural schematic diagram of the blank of the bearing cover, Figure 4 is another three - dimensional schematic diagram of the blank of the bearing cover, Figure 5 is a three - dimensional schematic diagram of the bearing cover, Figure 6 is another three - dimensional schematic diagram of the bearing cover. As Figure 1 and Figure 2 shown, this embodiment provides an intelligent control system and control method for a production line. This intelligent control system can monitor and intelligently analyze the manufacturing process of the bearing cover, automatically detect and classify the manufactured bearing covers, realizing automated production control, and at the same time improving production efficiency and quality. Specifically, it includes: a first monitoring device, a transportation component, a second monitoring device, and a system control device 80. The first monitoring device is used to detect whether the size of the mounting hole 12 and the flatness of the front end face 13 of the bearing cover 10 turned by the CNC lathe 30 are qualified, and is also used to monitor the spindle speed and tool feed speed of the CNC lathe 30. The transportation component is used to transport the blank 100 to be processed and the workpiece after detection to the intelligent storage unit 70 or the processing position. The second monitoring device is used to detect whether the size of the connection hole 11 drilled by the machining center 40 is qualified, and is also used to monitor the rotational speed information of the processing spindle of the machining center 40. The system control device 80 is used to receive the first detection signal sent by the first monitoring device and the second detection signal sent by the second monitoring device, and generate control instructions for the CNC lathe 30, the machining center 40, and the transportation component. The control instructions are encoded into control signals and transmitted to the corresponding CNC lathe 30, machining center 40, or transportation component. The system control device 80 is connected to the CNC lathe 30, the machining center 40, and the transportation component by wired or wireless means.

[0031] Specifically, the first monitoring device includes a sensing module, a timing module, and a first sub-control module deployed on the CNC lathe 30; the sensing module includes a laser measurement device, a magnetic induction encoder, and a displacement sensor. The laser measurement device includes a bracket and a line laser scanner disposed at the top of the bracket. The line laser scanner is used to collect three-dimensional point cloud data of the front end face 13 of the bearing cover 10. The magnetic induction encoder is used to detect the rotational speed information of the spindle, and the displacement sensor is used to detect the feed displacement information of the tool on the CNC lathe 30.

[0032] As Figure 10 shown, generally, a hydraulic chuck 200 for clamping a workpiece to be machined and a tool rest 300 opposite to the hydraulic chuck 200 are mainly arranged on the bed of the CNC lathe 30. The bracket is a robotic arm 400 installed on the CNC lathe 30. Taking the plane parallel to the front end face 13 of the bearing cover 10 as the X-Y plane and the plane perpendicular to the front end face 13 of the bearing cover 10 as the Z-Y plane to establish a coordinate system, the robotic arm 400 can move longitudinally along the Y axis, thereby driving the line laser scanner at the top to move and completing the scanning action of the front end face 13 of the bearing cover 10. The magnetic induction encoder is arranged at the motor bearing end cover of the spindle motor and can directly monitor the rotational speed information of the spindle. The displacement sensor uses a laser displacement sensor. The laser displacement sensor is arranged on the bed. The laser displacement sensor emits a beam to the tool and receives the reflected signal. According to the change of the reflected signal, the feed position and speed of the tool can be obtained. The timing module can record the working time parameters of the machine tool, assist in calculating the tool feed speed, and record the moment when the parameters change.

[0033] As Figure 1 and Figure 2As shown, the first sub-control module is used to analyze the collected sensing information, and then determine whether the quality of the machined workpiece is qualified, and analyze the current spindle parameter change information to determine whether there is a machine fault. If there is a fault, the system control device 80 controls the power supply of the CNC lathe 30 to be cut off for shutdown operation. At the same time, an alarm signal is sent to notify the management staff for maintenance. Specifically, the first sub-control module includes a flatness analysis module, a dimension analysis module, a parameter analysis module, and an information transmission module. The flatness tolerance of the part's plane shape is an important indicator reflecting the concave and convex height of the part's plane. The flatness analysis module is used to optimize the point cloud data of the acquired three-dimensional point cloud data through a filtering algorithm, extract the point cloud data of the front end face 13 of the bearing cover 10, segment the point cloud, traverse each segment of the point cloud, streamline the point cloud data and use the least squares method to fit the point cloud plane. Finally, traverse each point to obtain the point with the maximum distance from the fitting plane to obtain the flatness value, and compare the obtained flatness value with the preset feature threshold to determine whether the flatness is qualified. If it is qualified, a qualified signal is output, otherwise an unqualified signal is output; the dimension analysis module is used to traverse the point cloud data of the front end face 13 of the bearing cover 10, streamline the point cloud data by using the voxel grid method, and perform point cloud filtering based on the SOR filtering algorithm to obtain the edge feature information of the hole in the point cloud model. The edge data is fitted by the least squares method to determine the feature parameters of the hole. The feature parameters of the hole include the diameter of the fitted circle. The hole feature measurement is performed multiple times and the average value is taken as the reference value. The reference value is compared with the preset threshold to determine whether the size of the mounting hole 12 is qualified. If it is qualified, a qualified signal is output, otherwise an unqualified signal is output; the parameter analysis module is used to obtain the spindle speed information, compare the spindle speed information with the preset threshold to determine whether there is an abnormality, and if there is an abnormality, an abnormal alarm signal is output. It is also used to calculate the tool feed speed per unit time according to the displacement information, compare the tool feed speed with the preset threshold to determine whether there is an abnormality, and if there is an abnormality, an abnormal alarm signal is output. The information transmission module is used to send a detection signal to the system control device 80.

[0034] The transportation component includes an AGV cart component and a robotic arm component; the AGV cart component includes an AGV cart 60 and an AGV controller installed on the AGV cart 60. The AGV controller is used to control the AGV cart 60 to perform corresponding transportation work according to the transportation instructions sent by the system control device 80, and during the transportation process, use an obstacle avoidance algorithm based on deep learning for obstacle avoidance transportation.

[0035] The robotic arm component includes a six-axis robotic arm 50, a track 20, and a robot controller installed on the six-axis robotic arm 50. The six-axis robotic arm 50 is slidably installed on the track 20. The robot controller is used to control the six-axis robotic arm 50 to complete the transfer and installation operations of the blank 100 and the workpiece according to the transportation instructions sent by the system control device 80.

[0036] The second monitoring device includes a vision detection module, a Hall sensor, and a second sub-control module deployed in the machining center 40; the vision detection module includes an industrial camera, a light source module, and an image acquisition device. The industrial camera is used to collect images of the front end face of the drilled part, and the light source module is used to supplement light for the industrial camera. The light source module includes an annular LED lamp group and an LED dimming circuit. The annular LED lamp group is connected to the image acquisition device through the LED dimming circuit, and the LED dimming circuit is used to adaptively adjust the brightness of the LED light according to the control signal output by the image acquisition device. In this embodiment, the LED dimming circuit includes an LED driver chip, peripheral driving components, and a light sensor. The input end of the LED driver chip is electrically connected to the PWM output end of the image acquisition device. The light sensor is used to detect environmental light change information, and the image acquisition device can control the PWM signal for dimming by analyzing the light change. A diffuser plate is arranged around the annular LED lamp group.

[0037] Both the industrial camera and the light source module are connected to the image acquisition device. The image acquisition device is used to receive the analog image signal output by the industrial camera, convert it into a digital image signal through an A / D converter, control the photographing parameters of the industrial camera through the input / output interface, and store the digital image data.

[0038] The second sub-control module is used to analyze the collected image information, and then judge whether the quality of the processed workpiece is qualified, and analyze the current change information of the machining spindle speed to judge whether there is a machine fault. If there is a fault, the system control device 80 controls the power supply of the machining center 40 to be cut off for shutdown operation. At the same time, an alarm signal is sent to notify the management staff for maintenance.

[0039] When detecting the connection hole 11, place the drill workpiece to be detected in the detection area so that it is at the center of the field of view of the industrial camera. The image acquisition device sends a trigger pulse to the industrial camera, and the LED dimming circuit controls the LED lamp group for lighting compensation. The industrial camera acquires the image information of the drill workpiece to be detected, and converts it into a digital image signal through an A / D converter and inputs it into the second sub-control module for image processing and analysis. The second sub-control module includes an image processing module, a feature extraction module, and a classification output module. The image processing module acquires digital image data, and sequentially performs image gray-scale transformation processing, mean filtering processing, and binarization processing on the image data to obtain a segmented binary image; the feature extraction module uses an edge detection algorithm based on the Canny operator to perform edge detection on the preprocessed image, and then performs circle fitting by the least squares method. According to the circle fitting result, the diameter of the circle is calculated using the rectangular coordinate system method, and the aperture information of the connection hole 11 of the bearing cover 10 is obtained according to the formula r = p / q, where r is the conversion coefficient, p is the actual aperture measurement size, and q is the pixel distance; the classification output module is used to number each connection hole 11, compare the calculated aperture information of each connection hole 11 with a preset standard value, judge whether the aperture of the connection hole 11 is within the error range, classify each connection hole 11 according to the judgment result, and output the corresponding number and classification information of each connection hole 11 to the system control device 80; the second sub-control module also includes a comparison and analysis module. The Hall sensor is used to detect the rotational speed information of the machining spindle of the machining center 40. The Hall sensor inputs the detected information into the comparison and analysis module. The comparison and analysis module compares the rotational speed information of the machining spindle with a preset threshold value to judge whether there is an abnormality. If there is an abnormality, an abnormal alarm signal is output.

[0040] The system control device 80 includes a system controller, a data communication module, a database, and an interactive display module; the system controller includes a logic execution module and a production optimization module. The logic execution module is used to call the corresponding task execution logic according to the user interaction instruction information, and call the data execution task according to the task execution logic, and is also used to generate control instructions for the numerical control lathe 30, the machining center 40, and the transportation component; the production optimization module includes a safety assessment module and an optimization analysis module. The safety assessment module is used to obtain the index data of the production assessment and update and manage the index data, and perform a production process score using the hierarchical entropy analysis method based on the obtained index data and the index system constructed according to the first-level and second-level indexes corresponding to the production events.

[0041] In this embodiment, equipment failure and equipment usage data are collected to construct a secondary index system in dimensions such as production failure types, frequencies, and equipment usage information, and a primary index system based on equipment. The hierarchical entropy analysis method is used to calculate the weights of various indicators, and a production safety scoring model is constructed to achieve the assessment of regular production safety on the intelligent manufacturing production line for machining. The optimization analysis module is used to classify the production process risks according to the periodic change data of each optimization parameter, and match the production optimization suggestions in the expert solution library according to the optimization classification results. The optimization parameters include the spindle speed, machining spindle speed, and tool feed rate. For example, the historical spindle speed within three months is obtained, and the deviation value of the spindle speed within three months is calculated according to the preset speed value. The variance value of multiple groups of deviation values is calculated, and the variance value is compared with the empirical threshold to judge the risk classification level. If it is at the first level, the first-level production optimization suggestions in the expert solution library are matched according to the first-level coding information. If it is at the second level, the second-level production optimization suggestions in the expert solution library are matched according to the second-level coding information. The data communication module is used to provide a data communication link, and the data communication module is electrically connected to the system controller. The database is used to store system parameters and the monitored data collected and processed, and the database is electrically connected to the system controller. The interactive display module is electrically connected to the system controller. The interactive display module is used to receive the interactive instruction information of the user, and visually display the execution result and the system working parameters. The display interface can also display the graph of the workpiece to be measured, the standard value of the workpiece, and the measurement result. The interactive display module includes a touch display, which can realize the human-machine interaction function.

[0042] As Figure 7 shown, the machining process of the CNC lathe 30 includes: First, the blank 100 is taken out from the blank storage unit 71; Next, the blank 100 is loaded into the CNC lathe 30 for machining the front end face 13 and the mounting hole 12. The workpiece after turning the front end face 13 and the mounting hole 12 is called the turned part; Then, the size of the mounting hole 12 and the flatness of the front end face 13 are detected; According to the detection result, if the turned part is qualified, it is transferred and installed to the machining center 40 for subsequent machining; If it is unqualified, it is transferred to the non-conforming product storage unit 73. The whole process can ensure the quality of the turned part and improve the machining efficiency of the turned part.

[0043] As Figure 8 shown, the machining process of the machining center 40 includes: First, the qualified turned part is installed on the machining center 40, and then the operation of drilling the connecting hole 11 is carried out. The qualified turned part after drilling the connecting hole 11 is called the drilled part; Next, the size of the connecting hole 11 of the drilled part is strictly detected; If the detection result shows that the size is qualified, the finished bearing cover 10 will be transferred to the qualified product storage unit 72; If the size is unqualified, it will be transferred to the non-conforming product storage unit 73. The whole process ensures the traceability of product quality and machining efficiency.

[0044] AsFigure 9 As shown in the figure, this embodiment further includes an intelligent control method for a production line, which is applied to the above-mentioned intelligent control system for a production line and includes: Step S1: The system control device 80 obtains a system start trigger signal and initializes system control parameters. Then, it sends a control instruction to the AGV controller. The AGV controller controls the AGV trolley 60 to transport the blank 100 from the blank storage unit 71 to the transfer location and sends feedback information to the system control device 80. Then, the system control device 80 sends a transportation instruction to the robot controller. The robot controller controls the six-axis robotic arm 50 to complete the transfer and installation operations of the blank 100 and sends operation feedback information to the system control device 80; Step S2: The system control device 80 sends a control instruction to the CNC lathe 30. The CNC lathe 30 turns the front end face 13 and the mounting hole 12 of the bearing cover 10 according to a preset operation process. At the same time, it controls the first monitoring device to monitor the spindle speed and tool feed speed of the CNC lathe 30. After the operation is completed, the system control device 80 controls the first monitoring device to detect whether the size of the mounting hole 12 and the flatness of the front end face 13 of the bearing cover 10 turned by the CNC lathe 30 are qualified.

[0045] Specifically, the first monitoring device detecting whether the size of the mounting hole 12 and the flatness of the front end face 13 of the bearing cover 10 turned by the CNC lathe 30 are qualified includes: Using a pass-through filtering method to optimize the point cloud data of the acquired three-dimensional point cloud data, extracting the point cloud data of the front end face 13 of the bearing cover 10, segmenting the point cloud, traversing each segment of the point cloud, removing outliers, using a voxel grid method to downsample the point cloud data, and using the least squares method to fit the point cloud plane; Then, traverse each point to obtain the point with the maximum distance from the fitting plane, take the distance between this point and the fitting plane as the flatness value, and compare the obtained flatness value with a preset feature threshold to determine whether the flatness is qualified. If it is qualified, output a qualified signal; otherwise, output an unqualified signal; Traverse the point cloud data of the front end face 13 of the bearing cover 10, use a voxel grid method to downsample the point cloud data, and perform point cloud filtering based on the SOR filtering algorithm to obtain the edge feature information of the hole in the point cloud model; Perform least squares fitting on the edge data to determine the characteristic parameters of the hole. The characteristic parameters of the hole include the diameter of the fitted circle. Perform hole characteristic measurement multiple times and take the average value as a reference value. Compare the reference value with a preset threshold to determine whether the size of the mounting hole 12 is qualified. If it is qualified, output a qualified signal; otherwise, output an unqualified signal.

[0046] Step S3: The system control device 80 sends a signal to the robot controller according to the classification result. The robot controller controls the six-axis robotic arm 50 to complete the transfer and installation operations of the workpiece. Meanwhile, a control signal is sent to the AGV controller, and the AGV controller controls the AGV cart 60 to cooperate in transporting the non-conforming products to the non-conforming product storage unit 73. The intelligent warehousing unit 70 includes a blank storage unit 71, a qualified product storage unit 72, and a non-conforming product storage unit 73.

[0047] Step S4: The system control device 80 sends a control instruction to the machining center 40. The machining center 40 drills the connecting holes 11 of the bearing cover 10 according to a preset operation process. Meanwhile, the second monitoring device is controlled to monitor the rotational speed of the machining spindle of the machining center 40. After the operation is completed, the system control device 80 controls the second monitoring device to detect whether the dimensions of the connecting holes 11 of the bearing cover 10 are qualified; Specifically, the second monitoring device detecting whether the dimensions of the connecting holes 11 of the bearing cover 10 are qualified includes: Obtaining digital image data, and sequentially performing image gray-scale transformation processing, mean filtering processing, and binarization processing on the image data to obtain a segmented binarized image; Adopting an edge detection algorithm based on the Canny operator to perform edge detection on the preprocessed image, then performing circle fitting by the least squares method. According to the circle fitting result, the diameter of the circle is calculated using the rectangular coordinate system method, and the aperture diameter of the connecting hole 11 of the bearing cover 10 is obtained according to the formula r = p / q, where r is the conversion coefficient, p is the measured actual aperture size, and q is the pixel distance; Numbering each connecting hole 11, and comparing the calculated aperture information of each connecting hole 11 with a preset standard value to determine whether the aperture of the connecting hole 11 is within the error range. Classify each connecting hole 11 according to the judgment result, and output the corresponding number and classification information of each connecting hole 11 to the system control device 80.

[0048] Step S5: The system control device 80 sends a signal to the robot controller according to the classification result fed back by the second monitoring device. The robot controller controls the six-axis robotic arm 50 to complete the transfer operation of the drilled parts. Meanwhile, a control signal is sent to the AGV controller, and the AGV controller controls the AGV cart 60 to cooperate in transporting the qualified or non-conforming products to the intelligent warehousing unit 70 for classified placement.

[0049] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the embodiments of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. An intelligent control system for a production line, characterized in that, Including: A first monitoring device, configured to detect whether the dimensions of the mounting holes (12) and the flatness of the front end face (13) of the bearing cover (10) turned by the numerically controlled lathe (30) are qualified, and also configured to monitor the spindle speed and the tool feed speed of the numerically controlled lathe (30); A transportation component, configured to transport the workpiece blank (100) to be processed and the workpiece after detection to the intelligent storage unit (70) or the processing position; A second monitoring device, configured to detect whether the dimensions of the connecting holes (11) processed by the machining center (40) are qualified, and also configured to monitor the rotational speed information of the machining spindle of the machining center (40); A system control device (80), configured to receive the first detection signal sent by the first monitoring device and the second detection signal sent by the second monitoring device, and generate control instructions for the numerically controlled lathe (30), the machining center (40), and the transportation component.

2. The intelligent control system for a production line according to claim 1, characterized in that, The first monitoring device includes a sensing module, a timing module, and a first sub-control module deployed on the numerically controlled lathe (30); The sensing module includes a laser measurement device, a magnetic induction encoder, and a displacement sensor. The laser measurement device includes a bracket and a line laser scanner disposed at the top of the bracket. The line laser scanner is configured to collect three-dimensional point cloud data of the front end face (13) of the bearing cover (10). The magnetic induction encoder is configured to detect the spindle speed, and the displacement sensor is configured to detect the feed displacement of the tool on the numerically controlled lathe. The timing module is configured to record the working time of the machine tool; The first sub-control module includes a flatness analysis module, a dimension analysis module, a parameter analysis module, and an information transmission module. The flatness analysis module is used to optimize the acquired three-dimensional point cloud data through a filtering algorithm, extract the point cloud data of the front end face (13) of the bearing cover (10), segment the point cloud, traverse each segment of the point cloud, streamline the point cloud data, and fit the point cloud plane using the least squares method. Finally, traverse each point to obtain the point with the maximum distance from the fitted plane, and obtain the flatness value. Compare the obtained flatness value with a preset feature threshold to determine whether the flatness is qualified. If it is qualified, output a qualified signal; otherwise, output an unqualified signal. The dimension analysis module is used to traverse the point cloud data of the front end face (13) of the bearing cover (10), streamline the point cloud data using the voxel grid method, perform point cloud filtering based on the SOR filtering algorithm, obtain the edge feature information of the holes in the point cloud model, fit the edge data using the least squares method, and determine the feature parameters of the holes. The feature parameters of the holes include the diameter of the fitted circle. Perform hole feature measurement multiple times and take the average value as a reference value. Compare the reference value with a preset threshold to determine whether the size of the mounting hole (12) is qualified. If it is qualified, output a qualified signal; otherwise, output an unqualified signal. The parameter analysis module is used to obtain the spindle speed information, compare the spindle speed information with a preset threshold to determine whether there is an abnormality, and if there is an abnormality, output an abnormal alarm signal. It is also used to calculate the tool feed speed per unit time according to the displacement information, compare the tool feed speed with a preset threshold to determine whether there is an abnormality, and if there is an abnormality, output an abnormal alarm signal. The information transmission module is used to send detection signals to the system control device (80).

3. The intelligent control system for a production line according to claim 1, characterized in that, The transportation component includes an AGV cart component and a robotic arm component. The AGV cart component includes an AGV cart (60) and an AGV controller installed on the AGV cart. The AGV controller is used to control the AGV cart (60) to perform corresponding transportation work according to the transportation instructions sent by the system control device (80), and during transportation, use an obstacle avoidance algorithm based on deep learning for obstacle avoidance transportation. The robotic arm component includes a six-axis robotic arm (50), a track (20), and a robot controller installed on the six-axis robotic arm (50). The six-axis robotic arm (50) is slidably installed on the track (20). The robot controller is used to control the six-axis robotic arm (50) to complete the transfer and installation operations of the blank (100) and the workpiece according to the transportation instructions sent by the system control device (80).

4. The intelligent control system for a production line according to claim 1, characterized in that, The second monitoring device includes a vision detection module, a Hall sensor, and a second sub-control module deployed in the machining center (40). The visual detection module includes an industrial camera, a light source module, and an image acquisition device. The industrial camera is used to collect images of the front end face of the drilled part. The light source module is used to supplement light for the industrial camera. The light source module includes an annular LED lamp group and an LED dimming circuit. The annular LED lamp group is connected to the image acquisition device through the LED dimming circuit. The LED dimming circuit is used to adaptively adjust the brightness of the LED light according to the control signal output by the image acquisition device. The industrial camera and the light source module are both connected to the image acquisition device. The image acquisition device is used to receive the analog image signal output by the industrial camera, convert it into a digital image signal through an A / D converter, control the photographing parameters of the industrial camera through an input / output interface, and store the digital image data; The second sub-control module includes an image processing module, a feature extraction module, a classification output module, and a comparison and analysis module. The image processing module acquires the digital image data and sequentially performs image gray-scale transformation processing, mean filtering processing, and binarization processing on the image data to obtain a segmented binary image. The feature extraction module uses an edge detection algorithm based on the Canny operator to perform edge detection on the preprocessed image, then performs circle fitting by the least squares method. According to the circle fitting result, the diameter of the circle is calculated using the rectangular coordinate system method, and the aperture information of the connecting hole (11) of the bearing cover (10) is obtained according to the formula r = p / q, where r is the conversion coefficient, p is the actual aperture measurement size, and q is the pixel distance. The classification output module is used to number each connecting hole (11), compare the calculated aperture information of each connecting hole (11) with a preset standard value, determine whether the aperture of the connecting hole (11) is within the error range, classify each connecting hole (11) according to the judgment result, and output the corresponding number and classification information of each connecting hole (11) to the system control device (80); The Hall sensor is used to detect the rotational speed information of the machining spindle of the machining center (40). The Hall sensor inputs the detection information into the comparison and analysis module. The comparison and analysis module compares the rotational speed information of the machining spindle with a preset threshold value to determine whether there is an abnormality. If there is an abnormality, an abnormal alarm signal is output.

5. The intelligent control system for a production line according to claim 1, wherein The system control device (80) includes a system controller, a data communication module, a database, and an interactive display module; The system controller includes a logic execution module and a production optimization module. The logic execution module is used to call the corresponding task execution logic according to the user interaction instruction information, call data to execute tasks according to the task execution logic, and is also used to generate control instructions for the numerical control lathe, the machining center, and the transportation component. The production optimization module includes a safety assessment module and an optimization analysis module. The safety assessment module is used to obtain the index data of production assessment, update and manage the index data, and perform a production process score using the hierarchical entropy analysis method based on the obtained index data and the index system constructed according to the first-level and second-level indexes corresponding to production events. The optimization analysis module is used to classify the production process risks according to the periodic change data of each optimization parameter, and match the production optimization suggestions in the expert solution library according to the optimization classification results. The data communication module is used to provide a data communication link, and the data communication module is electrically connected to the system controller. The database is used to store system parameters and the monitored data collected and processed. The interactive display module is electrically connected to the system controller. The interactive display module is used to receive the user interaction instruction information, and visually display the execution result and the system working parameters.

6. An intelligent control method for a production line, which is applied to the intelligent control system for a production line according to any one of claims 1-5, and is characterized in that, Including: The system control device (80) obtains the system start trigger signal, initializes the system control parameters, and sends a control instruction to the AGV controller. The AGV controller controls the AGV trolley (60) to transport the blank (100) from the blank storage unit (71) to the transfer location and sends feedback information to the system control device (80). The system control device (80) sends a transportation instruction to the robot controller. The robot controller controls the six-axis robotic arm (50) to complete the transfer and installation operations of the blank (100) and sends operation feedback information to the system control device (80). The system control device (80) sends a control instruction to the numerical control lathe (30). The numerical control lathe (30) turns the front end face (13) and the mounting hole (12) of the bearing cover (10) according to the preset operation process. At the same time, it controls the first monitoring device to monitor the spindle speed and the tool feed speed of the numerical control lathe (30). After the operation is completed, the system control device (80) controls the first monitoring device to detect whether the size of the mounting hole (12) and the flatness of the front end face (13) of the bearing cover (10) turned by the numerical control lathe (30) are qualified. The system control device (80) sends a signal to the robot controller according to the classification result. The robot controller controls the six-axis robotic arm (50) to complete the transfer and installation operations of the workpiece, and sends a control signal to the AGV controller. The AGV controller controls the AGV trolley (60) to cooperate in transporting the non-conforming products to the non-conforming product storage unit (73). The system control device (80) sends control instructions to the machining center (40). The machining center (40) drills the connecting holes (11) of the bearing cover (10) according to a preset operation process. At the same time, it controls the second monitoring device to monitor the rotational speed of the machining spindle of the machining center (40), and after the operation is completed, the system control device (80) controls the second monitoring device to detect whether the dimensions of the connecting holes (11) of the bearing cover (10) are qualified; The system control device (80) sends a signal to the robot controller according to the classification result fed back by the second monitoring device. The robot controller controls the six-axis robotic arm (50) to complete the transfer operation of the drilled parts. At the same time, it sends a control signal to the AGV controller, and the AGV controller controls the AGV cart (60) to cooperate to transport the qualified or unqualified products to the intelligent storage unit (70) for classified placement.

7. The intelligent control method for a production line according to claim 6, wherein The first monitoring device detects whether the dimensions of the mounting holes (12) and the flatness of the front end face (13) of the bearing cover (10) turned by the CNC lathe (30) are qualified, including: The obtained three-dimensional point cloud data is optimized by the straight-through filtering method to extract the point cloud data of the front end face (13) of the bearing cover (10). The point cloud is segmented, each segment of the point cloud is traversed, the outlier points are removed, the point cloud data is thinned, and the least squares method is used to fit the point cloud plane; Finally, each point is traversed to obtain the point with the maximum distance from the fitting plane. The distance between this point and the fitting plane is used as the flatness value, and the obtained flatness value is compared with the preset feature threshold to judge whether the flatness is qualified. If it is qualified, a qualified signal is output, otherwise an unqualified signal is output; The point cloud data of the front end face (13) of the obtained bearing cover (10) is traversed, and the voxel grid method is used to thin the point cloud data. Then, based on the SOR filtering algorithm, point cloud filtering is performed to obtain the edge feature information of the holes in the point cloud model; The edge data is fitted by the least squares method to determine the feature parameters of the holes. The feature parameters of the holes include the diameter of the fitted circle. The hole feature measurement is performed multiple times and the average value is taken as the reference value. The reference value is compared with the preset threshold to judge whether the mounting hole dimensions are qualified. If it is qualified, a qualified signal is output, otherwise an unqualified signal is output.

8. The intelligent control method for a production line according to claim 6, wherein The second monitoring device detects whether the dimensions of the connecting holes (11) of the bearing cover (10) are qualified, including: The digital image data is obtained, and the image data is sequentially subjected to image gray-scale transformation processing, mean filtering processing, and binarization processing to obtain a segmented binary image; The preprocessed image is subjected to edge detection using the edge detection algorithm based on the Canny operator, and then circle fitting is performed by the least squares method. According to the circle fitting result, the diameter of the circle is calculated using the rectangular coordinate system method, and the aperture information of the connecting hole (11) of the bearing cover (10) is obtained according to the formula r = p / q, where r is the conversion coefficient, p is the actual aperture measurement size, and q is the pixel distance; Number each connection hole (11), compare the calculated aperture information of each connection hole (11) with a preset standard value to determine whether the aperture of the connection hole (11) is within the error range, classify each connection hole (11) according to the determination result, and output the corresponding number and classification information of each connection hole (11) to the system control device (80).

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