Spinneret plate batch detection system and method

Abstract

The invention relates to the technical field of spinneret plate detection in the chemical fiber industry, in particular to a batch type spinneret plate detection system and method.The system mainly comprises detection equipment, the equipment comprises a rack, a to-be-detected area is arranged on the table top of the rack and used for containing a material carrying disc, an infrared backlight source is arranged in the to-be-detected area, and the infrared backlight source is used for emitting infrared light; the infrared backlight source is positioned below the table board of the rack; mechanical arms are installed on the two sides outside the to-be-detected area, a blowing and sucking device and a line scanning camera array are installed on the mechanical arms, and the blowing and sucking device and the line scanning camera array are controlled by the mechanical arms to move, so that blowing and sucking cleaning and image detection are conducted on micropores of the spinneret plate distributed on the material carrying disc. The automatic level of spinneret plate detection is effectively improved, human intervention is reduced, the detection efficiency is improved, and the detection cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of spinneret detection in the chemical fiber industry, and in particular to a spinneret batch detection system and method. Background Art

[0002] The spinneret is an indispensable precision part in the spinning machine of the chemical fiber industry. In the production process of polyester filament high-speed spinning, mechanical impurities, gel, carbonization, thermal cracking and other particles in the melt often clog the micropores of the spinneret, resulting in uneven fineness of the spun silk, producing products such as "stigma silk", "fine silk" and "hairy silk". Therefore, the spinneret needs to be cleaned and inspected regularly.

[0003] At present, in the actual production process, non-contact semi-automatic microscopic inspection equipment is usually used to inspect spinnerets, such as the SpinTrak spinneret inspection system developed by ASPEX in the United States and the spinneret inspection instrument of ZENTES in Germany. This type of equipment locates and photographs the manually placed spinnerets hole by hole, and uses image processing methods to determine whether each micropore on the spinneret has dirt, wall burrs, and blockage, and marks the problematic micropores for manual processing. This type of microscopic inspection equipment has greatly improved the problems of slow speed, inconsistent standards, missed inspections, and other human factors in manual visual inspection methods, and has greatly improved the efficiency of spinneret inspection and the robustness of the results.

[0004] However, in the actual production process, it was found that the current spinneret microscopic inspection equipment has problems such as low automation and low detection efficiency: (1) The microscopic inspection equipment locates, photographs, and inspects each hole of the spinneret, with a slow detection speed and low efficiency; (2) The microscopic inspection equipment can only inspect circular holes but not special-shaped holes, and has certain limitations in use. Summary of the invention

[0005] In order to solve the above technical problems existing in the prior art, the present invention proposes a spinneret batch detection system and method, and its specific technical solution is as follows: A spinneret batch detection system mainly includes detection equipment, which includes: a frame, an inspection area is arranged on the frame table, the inspection area is used to place a material loading tray, an infrared backlight source is arranged in the inspection area, and the infrared backlight source is located under the frame table; mechanical arms are installed on both sides outside the inspection area, a blowing and suction device and a line scanning camera array are installed on the mechanical arms, and the blowing and suction device and the line scanning camera array are controlled by the mechanical arms to move, so as to blow and suck the micropores of the spinneret distributed on the material loading tray and perform image detection.

[0006] Preferably, the line scan camera array is composed of a plurality of line scan high-resolution industrial cameras connected in parallel, using a wide-angle telecentric lens, and detecting the state of each microhole of the spinneret based on machine vision and machine learning methods.

[0007] Preferably, the robotic arm adopts an XYZ three-axis control platform.

[0008] Preferably, the detection equipment also includes a display for outputting the qualified rate of micropore status of different types of spinnerets, different batches of spinnerets and different positions of spinnerets, and evaluating the quality of spinnerets and the content of micropore blockage and production process.

[0009] Preferably, it also includes automatic loading equipment and discharge packaging equipment. The automatic loading equipment identifies the model and number of each spinneret, and adjusts the posture based on the latch on the back of the spinneret, and places it on the spinneret loading tray in a specified direction; at the same time, the information of each spinneret model to be inspected on the loading tray is registered; the spinnerets are pushed to the inspection area of ​​the inspection equipment in units of loading trays; the discharge packaging equipment discharges, bags, seals, marks and puts into storage the qualified spinnerets on the loading tray.

[0010] Preferably, the loading tray is a six-hole tray, the holes of the tray are arranged in rows and columns, and the shape of the holes matches the frustum shape of the spinneret.

[0011] A sequential feeding spinneret detection method, comprising: Step 1, the automatic feeding equipment identifies the model and number of each spinneret, adjusts the posture based on the card slot on the back of the spinneret, and places it on the spinneret loading tray in the specified direction; at the same time, registers the information of each spinneret model to be inspected on the loading tray; pushes the spinneret to the inspection area of ​​the inspection equipment in units of the loading tray; Step 2: The robotic arm drives the line scan camera array to move at a constant speed to collect images of spinnerets distributed in rows and columns, and simultaneously detect the state of the micropores of each spinneret based on machine vision and machine learning methods; Step 3: When unqualified micropores are detected, the robotic arm drives the blowing and suction device to move to the top of the unqualified micropores to complete the blowing operation, and then moves the line scan camera array again to take local photos of the spinneret micropores; Step 4: If the local photo detection in step 3 is qualified, go to step 5; if it is still unqualified, repeat step 3 three times; if it is qualified within three times, go to step 5; if it is unqualified, go to step 6; Step 5: The qualified spinneret on the loading tray is discharged, bagged, sealed and marked by the discharging and packaging equipment; Step 6: Mark the unqualified spinnerets with images, display them on the display and notify manual processing.

[0012] The present invention is used to detect whether the micropores of various chemical fiber spinnerets have dirt, wall burrs, and blockage, effectively improving the automation level of spinneret detection, reducing human intervention, improving detection efficiency, and reducing detection costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a schematic diagram of the structure of the detection device of this embodiment; Figure 2 yes Figure 1 A top view of In the figure, 1- rack table, 2- loading tray, 3- robotic arm, 4- blowing and suction device, 5- line scan camera array, 6- display. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical scheme and technical effect of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0015] A spinneret batch detection system of the present embodiment mainly includes a detection device, on the side of which an automatic feeding device and a discharging packaging device can be installed, and the devices can be coordinated and controlled through a software program.

[0016] Specifically, the spinneret after cleaning and drying is placed in the loading area of ​​the automatic loading equipment. The automatic loading equipment can also be cascaded with the cleaning and drying equipment to realize automatic transfer of the spinneret between processes. The automatic loading equipment identifies the model and number of each spinneret, and adjusts the posture based on the snap-on on the back of the spinneret, and places it on the spinneret loading tray in the specified direction; at the same time, the information of each spinneret model to be inspected on the loading tray 2 is registered for use in subsequent microscopic inspection, unloading and packaging processes; the spinnerets are pushed to the detection equipment of this embodiment in units of loading tray 2 to queue for inspection.

[0017] like Figure 1 and Figure 2 As shown, the hardware part of the detection equipment in this embodiment includes: a frame, a waiting area for placing a material carrier 2 is set on the frame table 1, and an infrared backlight is set in the waiting area, and the infrared backlight is located under the frame table 1; mechanical arms 3 are installed on both sides outside the waiting area, and a blowing and suction device 4 and a line scan camera array 5 are installed on the mechanical arms 3. The mechanical arm 3 is an XYZ three-axis control platform, which is controlled by a high-precision servo motor, and cooperates with a high-resolution line scan camera array 5 and an infrared backlight to achieve one-time scanning imaging of all micropores on the spinneret, thereby improving imaging and detection efficiency. During the inspection process, the robot arm 3 drives the line scan camera to scan and image several spinnerets on the loading tray 2 in sequence, thereby realizing a round of inspection of the spinnerets; if the spinneret inspection is qualified, the discharging and packaging equipment will carry out the discharging, bagging, sealing, marking and storage; if a spinneret inspection is unqualified, the robot arm 3 moves the blowing device to the unqualified hole position, and after blowing, scans, photographs and inspects the spinneret again. After three repeated unqualified inspections, the position is marked on the image to notify manual processing.

[0018] Continue to refer Figure 2The material tray 2 is a six-hole tray, the shape of the holes matches the frustum of the spinneret, and each hole can be inlaid with a spinneret. Two material trays can be placed in the inspection area at the same time, with two material trays as a batch, and 12 spinnerets are arranged in four rows and three columns for inspection.

[0019] The infrared backlight source cooperates with the line scan camera array 5 to scan and image all the micropores on the spinneret at one time, thereby improving the imaging and detection efficiency. The line scan camera array 5 is composed of multiple line scan high-resolution industrial cameras connected in parallel, and adopts a wide-angle telecentric lens, which has the advantages of small distortion and high detection accuracy. The line scan camera array 5 associates the camera sampling frequency with the working frequency of the robotic arm servo motor based on the encoder. The line scan camera array 5 collects the spinneret image, and realizes the state detection of each micropore of the spinneret based on machine vision and machine learning methods. The detection items include micropore dirt, wall burrs, blockage conditions and special shaped hole sizes, etc. The detection accuracy reaches ±0.01mm, the false detection rate is 1%, and the missed detection rate is 1‰. By controlling the brightness of the infrared backlight source, different types of spinnerets can be detected, and the range of detectable micropores is: 0.01mm-5mm.

[0020] The software part of the detection device in this embodiment includes control software, detection software and statistical analysis software. The control software is responsible for the coordinated control of electrical equipment, motion equipment and sensors, realizes loading and unloading, micropore blowing and suction cleaning and other operations, and cooperates with the detection software to realize spinneret imaging and detection.

[0021] The detection software realizes functions such as spinneret image acquisition, micropore segmentation, edge detection, shape analysis, etc., and completes the task of detecting the micropore status of circular holes or irregular holes.

[0022] The statistical analysis software analyzes and visually outputs the qualified rates of micropores of different types of spinnerets, different batches of spinnerets and different positions of spinnerets based on the recorded detection results of each micropore on the spinneret through the display 6, evaluates the spinneret quality and the correlation between micropore blockage and production process, etc.

[0023] At present, the research and development budget of fully automatic microscopic inspection of a single spinneret is controlled within 1.5 million. The investment recovery period is related to the number of shared workshops. The more shared workshops there are, the better the effect of reducing manpower and the shorter the investment recovery period. If there are 2 to 4 shared workshops, the investment recovery period is about 2 to 6 years. In workshops that are not equipped with a single-machine microscopic inspection instrument, there are usually 6 microscopic inspectors who only work during the day shift; in workshops equipped with a single-machine microscopic inspection instrument, there are 3 microscopic inspectors, and 300 to 400 spinnerets can be inspected every day. Spinnerets of different specifications are manually placed in molds of corresponding specifications, and then placed on the automatic microscopic inspection instrument for inspection. The beat of the automatic microscopic inspection instrument is 22 to 30 minutes per 16 plates, and special-shaped holes still need to be manually inspected. After using the detection equipment of the present invention, only one person is retained in each workshop, and the detection beat is ≤14 minutes per 16 plates, and both circular holes and special-shaped holes can be detected, which is more practical and has a wider range of applications.

[0024] In summary, the detection device of this embodiment has the following effects: (1) Improve product quality and reduce the production of inferior products. It can detect and record each hole without omission. The detected spinning holes are judged in a digital way to ensure the correctness of the detection results, so as to achieve the best spinning quality and reduce the production of inferior products and unnecessary losses.

[0025] (2) Improve efficiency and save labor costs. The entire process of spinneret loading, microscopic inspection, discharging, packaging, and marking is fully automatically completed, greatly improving the efficiency of spinneret inspection. For unqualified holes, compressed air is used to automatically clean the dirt; only the spinning holes that cannot be cleaned by compressed air require manual follow-up processing.

[0026] (3) Understand the age and quality of the old spinneret. Since each spinneret has a test history, it is easy to understand the time the spinneret was online and the number of times it was used, and then evaluate the life of the spinneret.

[0027] (4) Evaluate the defect rate of cleaning equipment. Understand the cleaning status of the spinneret, and then improve the scheduling of the front-end cleaning equipment to increase the cleanliness of the spinneret.

[0028] (5) Real-time evaluation and improvement of abnormalities in the spinning process. If the dirty holes of the spinneret are found to be concentrated in a certain area, it may be a temperature problem or other problem in a certain process during the spinning process, and the process can be improved accordingly.

[0029] The above is only a preferred implementation case of the present invention and does not limit the present invention in any form. Although the implementation process of the present invention is described in detail above, for those familiar with the art, they can still modify the technical solutions recorded in the above examples, or replace some of the technical features therein with equivalents. All modifications, equivalent replacements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A spinneret batch detection system, mainly comprising a detection device, characterized in that: The detection device comprises: a frame, a waiting inspection area is arranged on the frame table (1), the waiting inspection area is used to place a material loading tray (2), an infrared backlight source is arranged in the waiting inspection area, and the infrared backlight source is located under the frame table (1); mechanical arms (3) are installed on both sides outside the waiting inspection area, and a blowing and suction device (4) and a line scanning camera array (5) are installed on the mechanical arms (3), and the blowing and suction device (4) and the line scanning camera array (5) are controlled by the mechanical arms (3) to move, so as to perform blowing and suction cleaning and image detection on the micropores of the spinneret distributed on the material loading tray (2).

2. The detection system according to claim 1, characterized in that: The line scan camera array (5) is composed of a plurality of line scan high-resolution industrial cameras connected in parallel, using a wide-angle telecentric lens, and detecting the state of each microhole of the spinneret based on machine vision and machine learning methods.

3. The detection system according to claim 1, characterized in that: The mechanical arm (3) adopts an XYZ three-axis control platform.

4. The detection system according to claim 1, characterized in that: The detection device also includes a display (6) for outputting the qualified rates of micropore states of different types of spinnerets, different batches of spinnerets, and different positions of the spinnerets, and evaluating the quality of the spinnerets and the content of micropore blockage and production process.

5. The detection system according to claim 1, characterized in that: It also includes automatic loading equipment and material discharging packaging equipment. The automatic loading equipment identifies the model and number of each spinneret, adjusts the posture based on the latch on the back of the spinneret, and places it on the spinneret loading tray (2) in a specified direction; at the same time, registers the information of the model of each spinneret to be inspected on the loading tray (2); the spinnerets are pushed to the inspection area of ​​the inspection equipment in units of the loading tray (2); and the material discharging packaging equipment discharges, bags, seals, marks and stores the qualified spinnerets on the loading tray (2).

6. The detection system according to claim 1, characterized in that: The material loading tray (2) is a six-hole tray, the tray holes are arranged in rows and columns, and the shape of the holes matches the frustum shape of the spinneret.

7. A spinneret detection method using the sequential feeding spinneret detection system according to any one of claims 1 to 6, characterized in that: include: Step 1, the automatic feeding device identifies the model and number of each spinneret, and adjusts the posture based on the bracket on the back of the spinneret, and places it on the spinneret loading tray (2) in a specified direction; at the same time, registers the information of the model of each spinneret to be inspected on the loading tray (2); and pushes the spinnerets to the inspection area of ​​the inspection device in units of the loading tray (2); Step 2, the robotic arm (3) drives the line scan camera array (5) to move at a constant speed, collects images of the spinnerets distributed in rows and columns, and simultaneously detects the state of the micropores of each spinneret based on machine vision and machine learning methods; Step 3: When unqualified micropores are detected, the robot arm (3) drives the blowing and suction device (4) to move above the unqualified micropores to complete the blowing operation, and then moves the line scan camera array (5) again to take local photos of the spinneret micropores for detection; Step 4: If the local photo detection in step 3 is qualified, go to step 5; if it is still unqualified, repeat step 3 three times; if it is qualified within three times, go to step 5; if it is unqualified, go to step 6; Step 5, the discharging and packaging equipment discharges, bags, seals and labels the qualified spinnerets on the loading tray (2); Step 6: Mark the image of the spinneret that fails the inspection, display it on the display (6) and notify manual processing.

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