Practical training platform based on hard alloy visual positioning

Abstract

The utility model discloses a practical training platform based on hard alloy visual positioning. The practical training platform mainly comprises a robot module, a feeding module, a conveying belt module, a visual module, an RFID detection module, a lathe feeding and discharging module and a visual assembling and stacking module. The feeding module is used for achieving directional output of materials and transferring the materials to the conveying belt module. The conveying belt module conveys the materials to a designated position; the visual module is used for performing image analysis on the materials; the RFID detection module is used for verifying and tracking the identity of the material; the lathe feeding and discharging module is used for achieving automatic feeding and discharging of materials. The visual assembling and stacking module is used for stacking the materials subjected to visual detection; and the robot module plans a grabbing path based on the material pose data provided by the vision module, and completes grabbing, rotating and placing actions. According to the utility model, by fusing machine vision, RFID technology and robot automation, automatic identification and accurate operation of hard alloy are realized; and meanwhile, teaching requirements can be met.

Description

TECHNICAL FIELD

[0001] The utility model relates to the automation detection and control technical field, and specifically relates to a kind of practical training platform based on hard alloy vision positioning. BACKGROUND

[0002] Hard alloy occupies an important position in modern industry, and is widely used in mechanical processing, aerospace, automobile manufacturing and many other fields. However, it requires very high positioning accuracy in processing. In traditional hard alloy processing, it relies on manual positioning or simple mechanical positioning, which has limited accuracy and is easily disturbed by human factors, and cannot meet the growing demand for high precision. With the rapid development of machine vision technology, its application in industrial automation is becoming more mature. The introduction of machine vision technology into hard alloy processing positioning can effectively overcome the drawbacks of traditional positioning methods.

[0003] Through real-time acquisition and analysis of workpiece images by the vision system, non-contact and high-precision positioning operations can be achieved. However, there is currently a lack of platforms on the market that are specifically designed for hard alloy and integrate teaching and practical training with actual processing and positioning functions. The practical training platform of the present application aims to fill this gap and promote the deep integration and innovative development of hard alloy processing technology and vision positioning technology. SUMMARY

[0004] To overcome the shortcomings of the prior art, the present utility model aims to provide a practical training platform based on hard alloy vision positioning, which can quickly and accurately identify the shape, size and position information of hard alloy workpieces with the help of advanced vision positioning technology, providing accurate positioning data for subsequent processing procedures, thereby greatly improving processing accuracy and efficiency and reducing scrap rates. It also provides a practical and effective training and research platform for related professionals, helping to cultivate professionals who master the application of cutting-edge vision positioning technology in hard alloy processing.

[0005] The technical solution adopted by the utility model to solve the technical problem is: a practical training platform based on hard alloy vision positioning, mainly including a robot module, a feeding module, a conveyor belt module, a vision module, an RFID detection module, a lathe feeding and discharging module, and a vision assembly and stacking module. The feeding module is used to realize directional output of materials and transfer them to the conveyor belt module. The conveyor belt module is used to convey materials to designated positions. The vision module mainly includes a vision camera and a light source for image analysis of materials. The RFID detection module is used to verify and track the identity of materials. The lathe feeding and discharging module is used to realize automatic feeding and discharging of materials. The vision assembly and stacking module is used to stack materials after vision detection. The robot module plans the grabbing path based on the material pose data provided by the vision module and completes grabbing, rotating and placing actions.

[0006] Further, the robot module is installed on a seventh shaft module, and the seventh shaft module is used to realize movement of the robot module in an X-axis range.

[0007] Further, the practical training platform comprises a quick-change module, and the quick-change module comprises a plurality of different clamps, and the quick-change joint is used to realize quick replacement of the clamps on the robot module.

[0008] Further, the practical training platform comprises a curved surface trajectory module, and the curved surface trajectory module is used to realize trajectory programming practice of the robot module on a curved surface; and the practical training platform comprises a drawing module, and the drawing module is used to realize trajectory data teaching programming of the robot module.

[0009] Further, the practical training platform comprises a rotating disc module, and the rotating disc module is used to realize rotary feeding by controlling rotation of a rotating disc through a stepping motor and detecting whether the material is in place through a sensor.

[0010] Further, the practical training platform comprises a positioner module, and a workpiece plane of the positioner module comprises a welding sheet metal and a cylinder tooling mechanism, and the welding sheet metal and the cylinder tooling mechanism are respectively used to realize welding and polishing functions of the positioner.

[0011] Further, the practical training platform further comprises a man-machine interface module, a display module, a gluing-polishing module and an assembly storage module.

[0012] Further, the feeding module is a well-type feeding module, and the well-type feeding module comprises a first feeding module and a second feeding module, and the two feeding modules are used to satisfy directional output of different types of materials; and the lathe feeding and discharging module comprises a rotary jaw cylinder and a jaw, and the jaw is clamped and rotated under the action of the rotary jaw cylinder.

[0013] Further, the visual assembly and stacking module comprises a plurality of stacking positions of different shapes, and the stacking positions of different shapes are distinguished by identification plates of different colors.

[0014] Further, the visual module adopts a deep learning algorithm to perform image analysis on the material, and a high-resolution visual camera is used to capture a hard alloy image in real time; and the visual module, the RFID detection module and the robot module adopt industrial Ethernet and TCP\IP communication to realize millisecond-level data synchronization therebetween.

[0015] The utility model discloses a beneficial effect is: compared with prior art, the utility model provides a kind of based on hard alloy visual positioning's practical training platform, is specially aimed at hard alloy and is integrated into the platform of teaching practical training and actual processing positioning function.By fusing machine vision, RFID technology and robot automation, realize the automatic identification and accurate operation of hard alloy parts, applicable to the industrial field of precision requirement strict of mechanical processing, aerospace etc..Meanwhile the equipment can also meet the task requirement of teaching, not only can promote the technical level of relevant practitioner, also can provide a series of solution ideas for alloy classification automation to enterprise. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The utility model provides the structural schematic diagram of platform.

[0017] Figure 2 The structural schematic diagram of seventh shaft module in the utility model.

[0018] Figure 3 The structural schematic diagram of quick-change module in the utility model.

[0019] Figure 4 The structural schematic diagram of curved surface trajectory module in the utility model.

[0020] Figure 5 The structural schematic diagram of positioner module in the utility model.

[0021] Figure 6 The structural schematic diagram of vision module in the utility model.

[0022] Figure 7 The structural schematic diagram of rotary disc module in the utility model.

[0023] Figure 8 The structural schematic diagram of feeding module in the utility model.

[0024] Figure 9 The structural schematic diagram of conveying belt module in the utility model.

[0025] Figure 10 The structural schematic diagram of lathe feeding and discharging module in the utility model.

[0026] Figure 11 The structural schematic diagram of vision assembly stacking module in the utility model.

[0027] Figure 12 The structural schematic diagram of RFID detection module in the utility model.

[0028] Wherein, 1 - visual module; 2-RFID detection module; 3 - lathe feeding and discharging module; 301 - rotary jaw cylinder; 302 - jaw; 4 - visual assembly stacking module; 401 - stacking position; 402 - identification plate; 5 - robot module; 6 - quick change module; 601 - quick change joint; 602 - clamp; 7 - human-computer interface module; 8 - drawing module; 9 - positioner module; 901 - tool plane; 902 - welded sheet metal; 903 - cylinder tool mechanism; 10 - seventh axis module; 11 - display module; 12 - rotary disc module; 1201 - rotary disc; 1202 - stepper motor; 13 - assembly storage module; 14 - curved track module; 15 - gluing-polishing module; 16 - feeding module; 1601 - first feeding module; 1602 - second feeding module; 17 - conveyor belt module. DETAILED DESCRIPTION

[0029] The utility model is further illustrated below by specific examples. However, these examples are only used to illustrate the utility model and not to limit the scope of the utility model.

[0030] Embodiment

[0031] As shown in Figures 1 to 12 , a practical training platform based on hard alloy visual positioning mainly includes robot module 5, feeding module 16, conveyor belt module 17, visual module 1, RFID detection module 2, lathe feeding and discharging module 3, visual assembly stacking module 4; the feeding module 16 is used to realize the directional output of materials, and the materials are transferred to the conveyor belt module 17; the conveyor belt module 17 is used to realize the conveying of materials to the designated position; the visual module 1 mainly includes a visual camera and a light source, and is used for image analysis of materials; the RFID detection module 2 is used to verify and track the identity of materials; the lathe feeding and discharging module 3 is used to realize the automatic feeding and discharging of materials; the visual assembly stacking module 4 is used to stack the materials after visual detection; the robot module 5 plans a grabbing path based on the material pose data provided by the visual module 1, and completes the grabbing, rotating and placing actions.

[0032] In one embodiment, the robot module 5 includes a base and a mechanical arm arranged on the base, the mechanical arm can rotate on the base, and the mechanical arm has multiple joints and can be adjusted up and down as needed. The base of the robot module 5 is installed on the seventh axis module 10, as shown in Figure 2 , the seventh axis module 10 is used to move the robot module 5 within the X-axis range, thereby increasing the working range of the robot module 5.

[0033] In one embodiment, the practical training platform includes a quick change module 6, as shown in Figure 3As shown, the quick-change module 6 includes several different clamps 602, such as a drawing pen, a trajectory pen, a pneumatic polishing pen, a workpiece gripper, a simulated welding gun, and a suction cup gripper; the quick-change connector 601 is used to quickly replace the clamps 602 on the robot module 5, so as to change the function of the robot and save costs.

[0034] In one embodiment, the training platform includes a curved surface trajectory module 14, as shown in Figure 4 The curved surface trajectory module 14 is used to realize trajectory programming practice on a curved surface, and provides triangular, square, circular, curved, elliptical, and pentagonal experimental trajectories, so as to realize relatively complex trajectory programming practice. The training platform includes a drawing module 8, which is used for the robot module 5 to realize trajectory data teaching programming; it is mainly used to learn how to use the teaching device to write a robot drawing program and complete writing or drawing on A4 paper.

[0035] In one embodiment, the training platform includes a rotary disc module 12, which is mainly used to place pin-like component modules and position the pins to facilitate the gripping of the mechanical arm. As shown in Figure 7 The rotary disc module 12 controls the rotation of the rotary disc 1201 through the stepping motor 1202, and is used to realize rotary feeding and detect whether the material is in place through a sensor.

[0036] In one embodiment, the training platform includes a positioner module 9, as shown in Figure 5 The positioner module 9 simulates a single-axis positioner, and the rotation of the positioner tool plane 901 is controlled by a servo motor. The positioner tool plane 901 includes a welding sheet metal 902 and a cylinder tool mechanism 903, which are used to realize the welding and polishing functions of the positioner, respectively.

[0037] In one embodiment, the training platform further includes a glue coating-polishing module 15 and an assembly storage module 13. The glue coating-polishing module 15 mainly simulates the function of using a mechanical arm to coat and polish a workpiece to be welded. The assembly storage module 13 stores the assembled components in a designated position after the mechanical arm assembles all the components, simulates the functions of stacking and unstacking, and achieves the teaching effect.

[0038] The training platform further includes a human-machine interface module 7 and a display module 11. The human-machine interface module 7 is a medium for interaction and information exchange between the system and the user, which realizes the conversion between the internal form of information and the form acceptable by humans, can control the start and stop of the entire teaching platform, and is the general control system of the experimental platform. The display module 11 can be used as an auxiliary module of the vision module 1.

[0039] In one embodiment, as shown in Figure 8 The feeding module 16 is a well-type feeding module, including a first feeding module 1601 and a second feeding module 1602, which are used for directional output of different types of materials. Figure 10 The lathe feeding and discharging module 3 includes a rotating clamp cylinder 301 and a clamp 302, which is clamped and rotated under the action of the rotating clamp cylinder 301. Figure 9 The conveying belt module 17 is driven by a stepping motor 1202, the motor speed can be adjusted, and the analog material can be directionally conveyed, and a sensor can detect whether the analog material reaches a specified position; a rotary encoder is provided on the conveying line to realize material tracking function.

[0040] In one embodiment, as shown in Figure 11 The visual assembly and stacking module 4 includes a plurality of different shapes of stacking positions 401, which are distinguished by different color identification plates 402. The visual assembly and stacking module 4 is provided with six different color identification plates 402, and after the workpiece is detected on the conveying belt, the robot moves the workpiece of the corresponding color and shape to the corresponding stacking position 401 for workpiece stacking.

[0041] In one embodiment, as shown in Figure 6 The visual module 1 uses a deep learning algorithm to analyze the image of the material, and a high-resolution visual camera is used to capture the image of the hard alloy in real time; the visual module 1, the RFID detection module 2 and the robot module 5 adopt industrial Ethernet and TCP\IP communication to realize millisecond-level data synchronization between each other.

[0042] The practical training platform based on visual positioning of hard alloy provided in the utility model is a platform specially for hard alloy and integrating teaching and practical training and actual processing positioning functions. It mainly relies on the visual algorithm of hard alloy, and collects image data of hard alloy in real time through a high-resolution industrial camera. A large hard alloy model library is built in the system, and these models are classified according to the shape, texture and other characteristics of different types of hard alloy. When the camera captures an image of hard alloy, the image processing module compares the image with the pictures in the model library, calculates the matching score through the algorithm, and judges the specific type of the alloy according to the matching score.

[0043] In order to ensure that the mechanical arm in the robot module 5 can accurately grasp and reasonably place the cemented carbide workpiece, the system takes the position and posture of the cemented carbide through a high-resolution industrial camera; by using image processing technology, the system can accurately calculate the rotation angle, displacement coordinates and other pose information of the workpiece. Through this accurate real-time collection, the system can provide key data for subsequent mechanical arm operation, ensuring that the mechanical arm can realize accurate grasping and placing in a complex production environment. Specifically, the collection of pose information not only helps to determine the position of the cemented carbide, but also monitors its rotation angle in real time, ensuring that the mechanical arm can adjust according to the specific posture of the workpiece. For example, during the conveying process of the cemented carbide, if the workpiece has displacement or rotation, the system will automatically detect this change and adjust the motion trajectory of the mechanical arm in real time through accurate coordinate and angle correction instructions.

[0044] The real-time collected pose and state data need to be transmitted to the mechanical arm on the pipeline for accurate positioning and operation. In order to achieve this goal, the system uses industrial Ethernet (Profinet) and TCP\IP communication to tightly connect the image recognition system and the mechanical arm control system. The data is transmitted to the mechanical arm control unit through a high-speed communication interface, and the mechanical arm performs corresponding actions according to the received coordinate information.

[0045] In order to further improve the degree of automation, the system introduces RFID tag technology. Embed RFID chips on each cemented carbide to store model, batch, processing parameters and other key information; in the sorting, stacking and storage link, verify the identity of the alloy through the RFID detection module 2 to ensure the traceability of the operation link, making the whole process more flexible and efficient. Through this process, the workload of traditional manual sorting and storage can be greatly simplified, work efficiency is improved, and human errors are avoided.

[0046] The above embodiments are only used to illustrate the present application, and are not intended to limit the present application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, therefore all equivalent technical solutions also belong to the scope of the present application, the patent protection scope of the present application should be defined by the claims.

Claims

1. A training platform based on carbide vision positioning, characterized in that: The system mainly includes a robot module, a feeding module, a conveyor belt module, a vision module, an RFID detection module, a lathe loading / unloading module, and a vision assembly and palletizing module. The feeding module is used to directionally output materials and transfer them to the conveyor belt module. The conveyor belt module is used to transport materials to designated locations. The vision module mainly includes a vision camera and a light source for image analysis of the materials. The RFID detection module is used to verify and track the identity of the materials. The lathe loading / unloading module is used to automatically load and unload materials. The vision assembly and palletizing module is used to palletize the materials after visual inspection. The robot module plans the grasping path based on the material pose data provided by the vision module and completes the grasping, rotation, and placement actions.

2. The training platform based on cemented carbide vision positioning as described in claim 1, characterized in that: The robot module is mounted on the seventh axis module, which is used to enable the robot module to move within the X-axis range.

3. The training platform based on cemented carbide vision positioning as described in claim 1, characterized in that: The training platform includes a quick-change module, which includes several different fixtures. The quick-change connector enables the rapid replacement of fixtures on the robot module.

4. The training platform based on cemented carbide vision positioning as described in claim 1, characterized in that: The training platform includes a surface trajectory module, which is used to enable the robot module to perform trajectory programming exercises on a curved surface; the training platform also includes a drawing module, which is used by the robot module to implement trajectory data teaching programming.

5. The training platform based on cemented carbide vision positioning as described in claim 1, characterized in that: The training platform includes a rotary disk module, which controls the rotation of the turntable through a stepper motor to achieve rotary feeding and uses sensors to detect whether the material is in place.

6. The training platform based on cemented carbide vision positioning as described in claim 1, characterized in that: The training platform includes a positioner module, and the tooling plane of the positioner module includes a welding sheet metal and a cylinder tooling mechanism, which are used to realize the welding and grinding functions of the positioner.

7. The training platform based on cemented carbide vision positioning as described in claim 1, characterized in that: The training platform also includes a human-machine interface module, a display module, an adhesive application-polishing module, and an assembly and storage module.

8. The training platform based on cemented carbide vision positioning as described in claim 1, characterized in that: The feeding module is a well-type feeding module, including a first feeding module and a second feeding module. The two feeding modules are used to meet the directional output of different types of materials. The lathe loading and unloading module includes a rotary gripper cylinder and grippers. The grippers are clamped and rotated under the action of the rotary gripper cylinder.

9. A training platform based on cemented carbide vision positioning as described in claim 1, characterized in that: The visual assembly and palletizing module includes several palletizing positions of different shapes, which are distinguished by different colored labels.

10. A training platform based on cemented carbide vision positioning as described in claim 1, characterized in that: The vision module uses deep learning algorithms to perform image analysis on the material and captures cemented carbide images in real time using a high-resolution vision camera. The vision module, RFID detection module and robot module use industrial Ethernet and TCP / IP communication to achieve millisecond-level data synchronization.

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