Visual identification-based X-ray nondestructive testing manual accurate sorting mistake proofing method

Abstract

The invention relates to the technical field of product classification, in particular to an X-ray nondestructive testing manual accurate sorting mistake proofing method based on visual identification, which realizes automatic sorting of detection results, optimizes the sorting process and improves the efficiency of detection and sorting by introducing a priority sequence and a secondary priority sequence. The phenomenon that images are left in X-ray detection imaging pictures by sensor wiring and routing is avoided, so that interference on reading and distinguishing of detection images is eliminated, and the detection accuracy is improved. A sensor does not need to be arranged on each detection tool, the tool cost is reduced, process videos before and after the samples are taken are recorded through the industrial camera, the taking sequence is judged based on the priority sequence and the process videos, the false alarm and missing report phenomena in the manual sorting process are reduced, the sorting accuracy is improved, and the working efficiency is improved. Therefore, the problem that an existing manual sorting mistake proofing method is low in accuracy is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of product classification, and in particular to a method for accurate manual sorting and error prevention based on visual recognition and X-ray non-destructive testing. Background Art

[0002] In the existing rotary table X-ray nondestructive testing equipment, when the inspection is completed and the products are manually sorted according to the results, photoelectric or laser sensors are generally used on the tooling to sense the presence of products, and LED lights are used to indicate the inspection results of the workpieces. In the actual application process, the following problems exist:

[0003] The sensor needs to be placed on the tooling and enter the inspection room together with the inspection platform and tooling. Due to the principle of X-ray non-destructive testing, the wiring and routing of the sensor can easily leave images in the X-ray inspection image, which may interfere with the reading and judgment of the inspection image.

[0004] For different samples, the detection tooling is different, and sensors need to be deployed on each tooling.

[0005] Sensors are consumables. After long-term high-frequency X-ray exposure, they are prone to failure and need to be replaced, affecting the equipment's detection and production efficiency.

[0006] When sorting materials manually, the actions of the picking process can easily interfere with the sensor sensing, causing false alarms in the sorting reminders. Summary of the invention

[0007] The purpose of the present invention is to provide a method for accurate manual sorting and error prevention in X-ray non-destructive testing based on visual recognition, aiming to solve the problem of low accuracy of existing manual sorting and error prevention methods.

[0008] To achieve the above object, the present invention provides a method for accurate manual sorting and error prevention of X-ray nondestructive testing based on visual recognition, comprising the following steps:

[0009] Testing the samples and outputting a priority sequence and a sub-priority sequence;

[0010] Recording the process video before and after the sample is taken by an industrial camera;

[0011] Determine the picking order based on the priority sequence and the process video;

[0012] Unloading materials based on the sub-priority sequence and judging the unloading process;

[0013] After unloading is completed, the status of the material tray is judged to complete the unloading operation.

[0014] Among them, in "testing samples and outputting priority sequence", it includes:

[0015] After testing the sample, the sample is discharged to a designated location;

[0016] A priority sequence and a sub-priority sequence are generated based on the samples.

[0017] Among them, "recording the process video before and after the sample is taken by an industrial camera" includes:

[0018] During the process of taking samples, the industrial camera triggers the snapshot function to record the process.

[0019] After taking the sample, the industrial camera takes a snapshot of the cutting tooling and records the status of the tooling.

[0020] Among them, in “determining the picking order based on the priority sequence and the process video”, it includes:

[0021] Use AI algorithms to identify the captured images of material removal tools to determine whether the material removal and material removal actions are correct;

[0022] If the material picking process is wrong, an alarm will be issued, and the sample restoration operation will be output to re-unload the material. If the material picking process is normal, the material will continue to be unloaded.

[0023] Among them, “cutting materials based on the sub-priority sequence and judging the cutting process” includes:

[0024] Collect the current status of the material tray and the view after unloading;

[0025] Based on the AI ​​algorithm and the secondary priority sequence, the current status diagram of the material tray and the view after unloading are judged. If they are wrong, an alarm is issued and the sample restoration operation is output to re-unload the material. If correct, unloading continues.

[0026] Among them, "judging the state of the material tray after unloading and completing the unloading operation" includes:

[0027] Collect the current status picture of the material tray;

[0028] The current status image is judged and detected. If there is no material in the material tray, the material is unloaded. If there is material in the material tray, an alarm is issued, and the samples in the priority sequence are restored and the material is unloaded again.

[0029] Among them, in "judging and detecting the current status picture, if there is no material in the material tray, unloading is completed; if there is material in the material tray, an alarm is issued, and the samples in the priority sequence are restored and unloading is started again", the alarm includes the sounding of an alarm buzzer and the flashing of an alarm light.

[0030] The method for accurate manual sorting and error prevention of X-ray nondestructive testing based on visual recognition of the present invention comprises the following steps: testing samples and outputting priority sequences and sub-priority sequences; recording process videos before and after the samples are taken by an industrial camera; judging the picking order based on the priority sequence and the process video; unloading materials based on the sub-priority sequence and judging the unloading process; judging the status of the material tray after unloading, and completing the unloading operation. The present invention realizes automatic sorting of detection results, optimizes the sorting process, and improves the efficiency of detection and sorting by introducing priority sequences and sub-priority sequences. The photoelectric or laser sensors on traditional tooling are abandoned, and the sensor wiring and routing are prevented from leaving images in the X-ray detection imaging pictures, thereby eliminating interference with the detection image recognition and discrimination, and improving the accuracy of detection. There is no need to deploy sensors on each inspection tooling, which reduces tooling costs and the trouble of re-deploying sensors due to tooling changes, improving the versatility and flexibility of the equipment. Industrial cameras are used to record process videos before and after sample retrieval, and the retrieval order is determined based on the priority sequence and process video, reducing false alarms and missed alarms in the manual sorting process, improving sorting accuracy, and thus solving the problem of low accuracy of existing manual sorting error prevention methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 It is a flow chart of the method for accurate manual sorting and error prevention of X-ray non-destructive testing based on visual recognition provided by the present invention.

[0033] Figure 2 It is a flow chart for testing samples and outputting priority sequence and sub-priority sequence.

[0034] Figure 3 It is a flow chart of recording the process video before and after the sample is taken by an industrial camera.

[0035] Figure 4 It is a flow chart for determining the picking order based on the priority sequence and the process video.

[0036] Figure 5 It is a flow chart for unloading materials based on the sub-priority sequence and judging the unloading process.

[0037] Figure 6 It is a flow chart for judging the status of the material tray after unloading and completing the unloading operation.

[0038] Figure 7 This is a schematic diagram of the result of the wrong material cutting sequence prompt.

[0039] Figure 8 It is a schematic diagram of the implementation steps of the method for accurate manual sorting and error prevention of X-ray non-destructive testing based on visual recognition provided by the present invention.

[0040] Fig. 9 It is a schematic diagram of workpiece detection. DETAILED DESCRIPTION

[0041] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0042] See also Figures 1 to 9 The present invention provides a method for accurate manual sorting and error prevention of X-ray non-destructive testing based on visual recognition, comprising the following steps:

[0043] S1 tests the samples and outputs a priority sequence and a sub-priority sequence;

[0044] S11 discharges the sample to a designated location after testing the sample;

[0045] Specifically, after the sample is tested on the turntable X-ray non-destructive testing equipment, the equipment automatically moves the sample to the preset discharge area based on the test results. The discharge area is equipped with sensors or mechanical devices to ensure that the sample can be accurately and stably placed in the designated position to facilitate subsequent operations.

[0046] S12 generates a priority sequence and a sub-priority sequence based on the samples.

[0047] Specifically, based on the test results, the system classifies the samples and generates a priority sequence and a sub-priority sequence. The priority sequence contains samples that need to be processed first, which may have a higher detection priority or require more urgent processing. The sub-priority sequence contains the remaining samples, which are processed after the priority sequence is processed.

[0048] S2 records the process video before and after the sample is taken by an industrial camera;

[0049] When S21 is taking samples, the industrial camera triggers the snapshot function to record the process.

[0050] Specifically, when the operator starts to pick up the sample, the industrial camera triggers the snapshot function to record the key frames in the process of picking up the sample. The snapshot function ensures that key information such as the operator's hand movements and the movement trajectory of the sample can be captured. This information is used to judge the correctness of the picking order and action later.

[0051] After taking the sample, the S22 industrial camera takes a snapshot of the cutting tooling and records the status of the tooling.

[0052] Specifically, after the sample is taken, the industrial camera captures the cutting tool and records the status of the tool. The captured image contains information such as the location and quantity of the sample on the tool. This information is used to judge the subsequent cutting process.

[0053] S3 determines the picking order based on the priority sequence and the process video;

[0054] S31 uses AI algorithms to identify the captured images of material removal tooling to determine whether the material picking and material removal actions are correct;

[0055] Specifically, the AI ​​algorithm performs image recognition and analysis on the captured images of the material removal tooling. By comparing the preset templates or standard images, it determines whether the material removal and material removal actions meet the requirements. If an error or abnormality is found in the action, the AI ​​algorithm will issue an alarm signal.

[0056] S32 If the material picking process is wrong, an alarm will be issued, and the sample restoration operation will be output to re-unload the material. If the material picking process is normal, the material will continue to be unloaded.

[0057] Specifically, if the AI ​​algorithm determines that the material handling process is wrong, the system will issue an alarm through the sounding of the alarm buzzer and the flashing of the alarm light. At the same time, the system will output the sample restoration operation instructions to guide the operator to put the sample back to its original position or perform other necessary processing. If the material handling process is normal, the system will continue with the unloading operation.

[0058] S4 performs material unloading based on the secondary priority sequence and judges the unloading process;

[0059] S41 collects the current status of the material tray and the view after unloading;

[0060] Specifically, during the unloading process, the industrial camera collects the current state image of the tray and the view after unloading in real time. These images are used for subsequent judgment of the unloading process.

[0061] S42 judges the current status diagram of the material tray and the view after unloading based on the AI ​​algorithm and the secondary priority sequence. If it is wrong, an alarm is issued and the sample restoration operation is output to re-unload the material. If it is correct, unloading continues.

[0062] Specifically, the AI ​​algorithm performs image recognition and analysis on the collected images. By comparing the preset template or standard image, it determines whether the material feeding process meets the requirements. If a material feeding error or abnormality is found, the AI ​​algorithm will issue an alarm signal and output a sample restoration operation instruction. If the material feeding process is normal, the system will continue with the subsequent operation.

[0063] S5 determines the status of the material tray after unloading is completed and completes the unloading operation.

[0064] S51 collects the current status picture of the material tray;

[0065] Specifically, after the unloading operation is completed, the industrial camera collects pictures of the current state of the tray. These pictures are used for subsequent judgment of the tray state.

[0066] S52 determines and detects the current status image. If there is no material in the material tray, the material is unloaded. If there is material in the material tray, an alarm is issued, and the samples in the priority sequence are restored and the material is unloaded again.

[0067] The alarm includes the sounding of an alarm buzzer and the flashing of an alarm light.

[0068] Specifically, the AI ​​algorithm performs image recognition and analysis on the collected pictures of the current status of the material tray.

[0069] If the judgment result is that there is no material in the tray, the system considers that the unloading operation has been completed and subsequent operations can be performed. If the judgment result is that there is material in the tray, the system will issue an alarm by sounding the alarm buzzer and flashing the alarm light. At the same time, the system will output the sample recovery operation instruction for the test priority sequence to guide the operator to put the sample back to its original position or perform other necessary processing.

[0070] Beneficial effects:

[0071] 1. No wiring is required on the tooling, and the X-ray imaging effect and result reading and judgment are not affected during detection.

[0072] 2. High adaptability: It does not limit the specific shape of the sample workpiece.

[0073] 3. High economic efficiency: For different detection tools used for different samples, there is no need to increase the number of sensors on the tools.

[0074] 4. Long service life. Professional industrial cameras are used for visual recognition, which have good tolerance to various harsh conditions in the equipment production environment. They are non-destructive parts and do not need to be replaced frequently.

[0075] The above disclosure is only a preferred embodiment of the method for accurate manual sorting and error prevention of X-ray non-destructive testing based on visual recognition of the present invention. Of course, this cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiments and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A method for accurate manual sorting and error prevention based on visual recognition of X-ray non-destructive testing, characterized by: The following steps are involved: Testing the samples and outputting a priority sequence and a sub-priority sequence; Recording the process video before and after the sample is taken by an industrial camera; Determine the picking order based on the priority sequence and the process video; Unloading materials based on the sub-priority sequence and judging the unloading process; After unloading is completed, the status of the material tray is judged to complete the unloading operation.

2. The method for accurate manual sorting and error prevention of X-ray nondestructive testing based on visual recognition as claimed in claim 1, characterized in that: In "Test samples and output priority sequence", include: After testing the sample, the sample is discharged to a designated location; A priority sequence and a sub-priority sequence are generated based on the samples.

3. The method for accurate manual sorting and error prevention of X-ray nondestructive testing based on visual recognition as claimed in claim 1, characterized in that: In "recording the process video before and after the sample is taken by an industrial camera", it includes: During the process of taking samples, the industrial camera triggers the snapshot function to record the process. After taking the sample, the industrial camera takes a snapshot of the cutting tooling and records the status of the tooling.

4. The method for accurate manual sorting and error prevention of X-ray nondestructive testing based on visual recognition as claimed in claim 1, characterized in that: In "determining the picking order based on the priority sequence and the process video", it includes: Use AI algorithms to identify the captured images of material removal tools to determine whether the material removal and material removal actions are correct; If the material picking process is wrong, an alarm will be issued, and the sample restoration operation will be output to re-unload the material. If the material picking process is normal, the material will continue to be unloaded.

5. The method for accurate manual sorting and error prevention of X-ray nondestructive testing based on visual recognition as claimed in claim 1, characterized in that: In "unloading materials based on the sub-priority sequence and judging the unloading process", it includes: Collect the current status of the material tray and the view after unloading; Based on the AI ​​algorithm and the secondary priority sequence, the current status diagram of the material tray and the view after unloading are judged. If they are wrong, an alarm is issued and the sample restoration operation is output to re-unload the material. If correct, unloading continues.

6. The method for accurate manual sorting and error prevention of X-ray nondestructive testing based on visual recognition as claimed in claim 1, characterized in that: In "judging the status of the material tray after unloading and completing the unloading operation", it includes: Collect the current status picture of the material tray; The current status image is judged and detected. If there is no material in the material tray, the material is unloaded. If there is material in the material tray, an alarm is issued, and the samples in the priority sequence are restored and the material is unloaded again.

7. The method for accurate manual sorting and error prevention of X-ray nondestructive testing based on visual recognition as claimed in claim 6, characterized in that: In "judging and detecting the current status image, if there is no material in the material tray, unloading is completed; if there is material in the material tray, an alarm is issued, and the samples in the test priority sequence are restored and unloading is started again", the alarm includes the sounding of an alarm buzzer and the flashing of an alarm light.

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