Feeding mechanism for PCB (Printed Circuit Board) maintenance

Abstract

The invention discloses a feeding mechanism for PCB maintenance, which comprises a first lifting frame, the bottom of one side of the first lifting frame is provided with a pushing port, and the top of the other side, opposite to the pushing port, of the first lifting frame is provided with a feeding port. And a first lifter extending from the propelling opening to the feeding opening is arranged in the first lifting frame and comprises a ball screw mechanism driven by double servo motors, a self-adaptive clamp with a pressure feedback function, an air cushion-silica gel composite damping layer and an intelligent control system integrated with machine vision. According to the feeding mechanism for PCB maintenance, previous feeding and subsequent maintenance procedures are connected through the first lifter, automatic circuit board conveying is achieved, and therefore full automation of a maintenance line is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit board maintenance, and particularly to a feeding mechanism for PCB circuit board maintenance. Background Art

[0002] At present, the maintenance of PCB circuit boards in production relies heavily on manual labor, with low efficiency and excessive dependence on the technical level of workers, making it unable to adapt to the modern reform of intelligent manufacturing. Summary of the Invention

[0003] To solve the deficiencies in the prior art, the present invention provides a feeding mechanism for PCB circuit board maintenance. This feeding mechanism for PCB circuit board maintenance is an intelligent lifting device with functions of adaptive adjustment, multi-sensor feedback, and shock absorption, which can effectively improve the feeding efficiency and the yield rate of circuit boards.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] The present invention provides a feeding mechanism for PCB circuit board maintenance, including a first lifting frame. A pushing port is provided at the bottom of one side of the first lifting frame, and a feeding port is provided at the top of the other side of the first lifting frame opposite to the pushing port. A first lifter extending from the pushing port to the feeding port is arranged inside the first lifting frame. The first lifter includes a ball screw mechanism driven by a servo double motor, an adaptive fixture with pressure feedback, an air cushion-silica gel composite shock absorption layer, and an intelligent control system integrating machine vision.

[0006] Further, a feeding slideway is arranged at the position of the first lifting frame corresponding to the feeding port. A feeding paddle is arranged on the feeding slideway, and a power part is arranged on the feeding slideway.

[0007] Further, no less than 8 groups of infrared photoelectric sensors are arranged at the edge of the fixture to form an annular detection area.

[0008] Further, the shock absorption system includes a vibration prediction algorithm trained based on an LSTM neural network.

[0009] Advantages are as follows:

[0010] 1. The present invention is an intelligent lifting device with functions of adaptive adjustment, multi-sensor feedback, and shock absorption, which can effectively improve the feeding efficiency and the yield rate of circuit boards. Description of the Drawings

[0011] Figure 1 It is a side view of the feeding mechanism for PCB circuit board maintenance according to an embodiment of the present invention.

[0012] 411. First lifting frame; 412. First lifter; 414. Loading slide; 415. Loading paddle; 416. Push port; 417. Loading port. DETAILED DESCRIPTION

[0013] The present invention will be further described below in conjunction with the accompanying drawings:

[0014] Example:

[0015] A feeding mechanism for PCB circuit board maintenance, such as Figure 1 As shown, it includes a first lifting frame 411, a thrust port 416 is provided at the bottom of one side of the first lifting frame 411, a loading port 417 is provided at the top of the other side of the first lifting frame 411 opposite to the thrust port 416, and a first lifter 412 is provided inside the first lifting frame 411, extending from the thrust port 416 to the loading port 417, and the first lifter 412 includes a ball screw mechanism driven by a servo dual motor, an adaptive clamp with pressure feedback, an air cushion-silicone composite shock-absorbing layer and an intelligent control system with integrated machine vision.

[0016] In the maintenance line of the flexible circuit board, the conveying mechanism first delivers the loading platform carrying the PCB circuit board from the push port 416. At this time, the first lifter 412 is located at the push port 416, and the loading platform enters the first lifter 412 from the push port 416, and then is lifted to the loading port 417. Then, the loading platform is pushed out from the loading port 417 and enters the table conveyor of the maintenance system, so that the maintenance station can be moved by relying on the table conveyor.

[0017] The feeding mechanism for PCB circuit board maintenance connects the aforementioned feeding and subsequent maintenance processes through the first lifter 412, realizes automatic circuit board transportation, and thus realizes full automation of the maintenance line.

[0018] In another embodiment, the first lifting frame 411 is provided with a loading chute 414 at a position corresponding to the loading port 417 , a loading paddle 415 is provided on the loading chute 414 , and a power unit is provided on the loading chute 414 .

[0019] The material loading platform is pushed out from the first lifter 412 by the loading paddle 415 moving the material loading platform.

[0020] In another embodiment, no less than 8 groups of infrared photoelectric sensors are provided on the edge of the fixture to form a ring-shaped detection area.

[0021] In another embodiment, the vibration reduction system includes a vibration prediction algorithm based on LSTM neural network training.

[0022] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. A feeding mechanism for PCB circuit board maintenance, characterized in that, It includes a first lifting frame. A propulsion port is provided at the bottom on one side of the first lifting frame, and a feeding port is provided at the top on the other side of the first lifting frame relative to the propulsion port. A first lifter extending from the propulsion port to the feeding port is arranged inside the first lifting frame. The first lifter includes a ball screw mechanism driven by a servo dual-motor, an adaptive fixture with pressure feedback, an air cushion-silicone composite shock-absorbing layer, and an intelligent control system integrating machine vision.

2. The feeding mechanism for PCB circuit board maintenance according to claim 1, characterized in that, A feeding slideway is arranged at the position of the first lifting frame corresponding to the feeding port. A feeding paddle is arranged on the feeding slideway, and a power part is arranged on the feeding slideway.

3. The feeding mechanism for PCB circuit board maintenance according to claim 1, wherein, No less than 8 groups of infrared photoelectric sensors are arranged at the edge of the fixture to form an annular detection area.

4. The feeding mechanism for PCB circuit board maintenance according to claim 1, wherein, The shock-absorbing system includes a vibration prediction algorithm trained based on the LSTM neural network.

Images