A sewing skip detection device
By using a sewing skip detection device that combines an electric photometer sensor and an encoder with an AI algorithm chip, the problems of easy damage and slow detection speed of existing devices have been solved, achieving efficient and accurate skip detection and reducing production costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- SHANGHAI QQ MICROELECTRONICS TECHNOLOGY CO LTD
- Filing Date
- 2025-09-09
- Publication Date
- 2026-07-31
AI Technical Summary
In existing sewing machine skip detection devices, piezoelectric sensors, tension sensors, and roller encoders are easily damaged because they are in direct contact with the thread. Miniature cameras have slow detection speeds, making it difficult to meet the needs of high-speed sewing, resulting in insufficient detection accuracy and lifespan.
By replacing piezoelectric sensors, tension sensors, and roller encoders with photoelectric sensors and encoders, and combining them with AI algorithm chips, the sewing machine spindle signals are collected in real time through non-contact image sensors, enabling rapid detection.
It improves detection accuracy and speed, avoids mechanical wear, reduces production costs, and ensures synchronous detection and zero-defect production during the sewing process.
Smart Images
Figure CN224578467U_ABST
Abstract
Description
Technical Field
[0001] This utility model belongs to the field of sewing machine skipped stitch detection technology, and specifically relates to a sewing machine skipped stitch detection device. Background Technology
[0002] In the sewing machine manufacturing process, skipped stitches are one of the common sewing defects. Skipped stitches prevent the thread from forming a proper stitch, affecting not only the appearance quality of the sewn product but also reducing its durability and increasing subsequent rework costs. This is especially true in the production of airbags, where most are sewn with polyamide thread. Due to the safety requirements of airbags, the sewing process must be strictly controlled, adhering to the principle of zero defects. Any product with broken thread or empty needle will be scrapped. For safety reasons, the sewing machine must be able to promptly alarm for skipped stitches and broken thread defects to ensure that airbags leave the factory with zero defects. However, existing skipped stitch detection devices typically use piezoelectric sensors, tension sensors, roller encoders, and miniature cameras for detection. When using piezoelectric sensors for detection, the time the surface occupies during the feeding process within a preset time period is obtained by directly contacting the piezoelectric sensor with the surface line, thereby determining whether there is a skipped line. However, since the piezoelectric sensor needs to be in continuous contact with the moving surface line, the surface line will continuously rub and impact the sensor during the high-speed feeding process, resulting in rapid sensor wear and short service life, which cannot meet the needs of enterprises for long-term continuous production. When using a tension sensor for detection, the tension sensor is in direct contact with the surface thread to monitor the tension changes during the feeding process of the surface thread within a preset time period. The skipped needle situation is judged based on the abnormal tension fluctuations. However, similar to piezoelectric sensors, the direct contact design of the tension sensor with the surface thread makes the sensor susceptible to surface thread wear and oil contamination during long-term use. This not only reduces the tension detection accuracy but also significantly shortens the sensor's lifespan. When using a roller encoder for detection, the roller encoder directly contacts the surface line, and the feed of the surface line drives the encoder roller to rotate, thereby obtaining the change in the length of the surface line within a preset time period, which is used to identify skipped lines. However, the direct contact between the roller and the surface line will also cause mechanical wear between the two components, and the roller will experience surface wear and slippage after long-term use, which will affect the subsequent detection results. When using a miniature camera for inspection, non-contact inspection is performed by capturing images of the thread feeding process and using image recognition technology to determine whether there is a skipped stitch. However, the image acquisition speed of the miniature camera is slow, which is difficult to match the needs of high-speed sewing of the sewing machine, and is prone to detection delay, resulting in missed detection of skipped stitch defects. Therefore, there is an urgent need for a sewing skip detection device to solve the above problems. Utility Model Content
[0003] In view of the problems mentioned above in the background art, the purpose of this utility model is to provide a sewing skip detection device.
[0004] To achieve the above-mentioned technical objectives, the technical solution adopted by this utility model is as follows: A sewing skip detection device includes a base, a bobbin unit, and a bobbin cup unit. A bracket is installed on one side of the top of the base, and an electric photoelectric sensor is installed on one side of the top of the bracket. The electric photoelectric sensor is signal-connected to a computer motherboard unit, which is installed inside the base. The computer motherboard unit is signal-connected to an audible and visual alarm unit and an encoder, both of which are installed inside the base.
[0005] Furthermore, the photoelectric sensor is positioned with a downward view of the bobbin unit and the bobbin cup unit at a distance of 2mm to 6mm. This structural design ensures that the photoelectric sensor can be aligned with the bobbin unit from above.
[0006] Further specifying, the computer motherboard unit is an industrial control motherboard integrating an AI algorithm chip. The motherboard unit has multiple interfaces, which are electrically connected to the photoelectric sensor, the audible and visual alarm unit, and the encoder via data cables and signal cables. This structural design allows the motherboard unit to receive signals transmitted from the encoder, determine a preset detection time period, and simultaneously receive the surface feed image from the photoelectric sensor. The built-in AI skip-pin recognition algorithm processes the image to determine the presence of skip-pins, and sends an alarm signal to the audible and visual alarm unit when a skip-pin is detected.
[0007] Furthermore, the audible and visual alarm unit includes an LED warning light and a buzzer. The LED warning light is mounted on the outside of the base, and the buzzer is mounted inside the base. With this structural design, when the computer motherboard unit sends an alarm signal, the LED warning light flashes red, and the buzzer emits a continuous alarm sound to alert the operator.
[0008] The beneficial effects of this utility model are as follows: This utility model replaces existing piezoelectric sensors, tension sensors, and roller encoders with an electric eye unit (non-contact image sensor) and an encoder unit (connected to the sewing machine spindle but not in direct contact with the thread), avoiding direct contact between the detection components and the thread, significantly reducing mechanical wear. Furthermore, by using the combination of the electric eye photometer sensor and the encoder, the sewing machine spindle signal is collected in real time, which can quickly determine the preset detection time period and ensure that the detection is synchronized with the sewing process. At the same time, the computer motherboard unit integrates a high-performance AI algorithm chip, which greatly improves the detection speed and efficiency and avoids missed detection of skipped stitch defects. This utility model not only has a simple mechanical structure, but also incorporates AI algorithms to achieve the identification and accurate positioning of skipped stitches. It is also easy to install, fast to measure, economical, efficient, and reliable, reducing the production cost of product defect detection. Attached Figure Description
[0009] This utility model can be further illustrated by the non-limiting embodiments given in the accompanying drawings; Figure 1 This is a schematic diagram of the structure of a sewing skip detection device according to an embodiment of the present invention; The symbols for the main components are explained below: Base 1, bobbin unit 10, bobbin unit 11, bracket 2, photoelectric sensor 3, computer motherboard unit 4, audible and visual alarm unit 5, LED warning light 51, buzzer 52, encoder 6. Detailed Implementation
[0010] To enable those skilled in the art to better understand this utility model, the technical solution of this utility model will be further described below in conjunction with the accompanying drawings and embodiments.
[0011] like Figure 1 As shown, the sewing skip detection device of this utility model includes a base 1, a bobbin unit 10 and a bobbin unit 11. A bracket 2 is installed on one side of the top of the base 1, and an electric photoelectric sensor 3 is installed on one side of the top of the bracket 2. The electric photoelectric sensor 3 is connected to a computer motherboard unit 4. The computer motherboard unit 4 is installed inside the base 1. The computer motherboard unit 4 is connected to an audible and visual alarm unit 5 and an encoder 6. Both the audible and visual alarm unit 5 and the encoder 6 are installed inside the base 1.
[0012] Preferably, the photoelectric sensor 3 is positioned with a downward view of the bobbin unit 10 and the bobbin cup unit 11, at a distance of 2mm to 6mm. This structural design ensures that the photoelectric sensor can be aligned with the bobbin unit from above. In practice, other mounting structures for the photoelectric sensor 3 and the bobbin unit 10 can also be considered depending on the specific circumstances.
[0013] Preferably, the computer motherboard unit 4 is an industrial control motherboard integrating an AI algorithm chip. The computer motherboard unit 4 has multiple interfaces, which are electrically connected to the photoelectric sensor 3, the audible and visual alarm unit 5, and the encoder 6 via data cables and signal cables. This structural design allows the computer motherboard unit 4 to receive signals transmitted from the encoder 6, determine the preset detection time period, and simultaneously receive the surface feed image from the photoelectric sensor 3. The built-in AI skip-pin recognition algorithm processes the image to determine if skip-pins exist, and sends an alarm signal to the audible and visual alarm unit 5 when a skip-pin is detected. In practice, other control structures can also be considered for the computer motherboard unit 4 depending on the specific circumstances.
[0014] Preferably, the audible and visual alarm unit 5 includes an LED warning light 51 and a buzzer 52. The LED warning light 51 is mounted on the outside of the base 1, and the buzzer 52 is mounted inside the base 1. With this structural design, when the computer motherboard unit 4 sends an alarm signal, the LED warning light 51 flashes red light, and the buzzer 52 emits a continuous alarm sound to alert the operator. In practice, other structural shapes of the audible and visual alarm unit 5 can also be considered depending on the specific circumstances.
[0015] In this embodiment, during use, the photoelectric sensor 3 is used to view the bobbin unit 10 from above at a distance of 2mm to 6mm to clearly capture the feeding state of the surface line of the bobbin unit 10. The photoelectric sensor 3 is electrically connected to the computer motherboard unit 4 via a data cable to transmit the captured surface line feeding image of the bobbin unit 10 to the computer motherboard unit 4. When the computer motherboard unit 4 receives the surface line feeding image from the photoelectric sensor 3, it processes the image using a built-in AI skip-pin recognition algorithm to determine whether a skip-pin exists. When a skip-pin is detected, an alarm signal is sent to the audible and visual alarm unit. When the computer motherboard unit 4 sends the alarm signal, the audible and visual alarm unit 5 starts working and emits rhythmic light and sound according to a specific pattern. The LED warning light 51 emits a red flashing light, and the buzzer 52 sounds. At the same time, the computer motherboard unit 4 records the time of the skip-pin occurrence and the spindle angle to achieve precise positioning of the skip-pin.
[0016] The encoder 6 is electrically connected to the computer motherboard unit 4 via a signal line. It is used to collect the speed signal and rotation angle signal of the sewing machine spindle in real time and transmit the signal to the computer motherboard unit 4 to determine the preset detection time period.
[0017] The computer motherboard unit 4 is also equipped with a data storage module, which is used to store the face line feed images and jump pin detection records within a preset time period for easy subsequent query and traceability. At the same time, the computer motherboard unit is equipped with a USB interface, which can be connected to external devices (such as computers and USB flash drives) via USB data cable to realize data export and algorithm update.
[0018] The above embodiments are merely illustrative of the principles and effects of this utility model and are not intended to limit the scope of this utility model. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this utility model. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this utility model should still be covered by the claims of this utility model.
Claims
1. A sewing skip detection device, comprising a base (1), a bobbin unit (10), and a bobbin unit (11), characterized in that: A bracket (2) is installed on one side of the top of the base (1). A photoelectric sensor (3) is installed on one side of the top of the bracket (2). The photoelectric sensor (3) is connected to a computer motherboard unit (4). The computer motherboard unit (4) is installed inside the base (1). The computer motherboard unit (4) is connected to an audible and visual alarm unit (5) and an encoder (6). Both the audible and visual alarm unit (5) and the encoder (6) are installed inside the base (1).
2. The sewing skip detection device according to claim 1, characterized in that: The photoelectric sensor (3) looks down at the bobbin unit (10) and the bobbin unit (11) at a distance of 2mm to 6mm.
3. The sewing skip detection device according to claim 2, characterized in that: The computer motherboard unit (4) is an industrial control motherboard with an integrated AI algorithm chip. The computer motherboard unit (4) has multiple interfaces, which are electrically connected to the photoelectric sensor (3), the sound and light alarm unit (5) and the encoder (6) through data lines and signal lines, respectively.
4. The sewing skip detection device according to claim 3, characterized in that: The sound and light alarm unit (5) includes an LED warning light (51) and a buzzer (52). The LED warning light (51) is installed on the outside of the base (1), and the buzzer (52) is installed inside the base (1).