Integrated multi-component coupling packaging equipment

Abstract

The invention discloses integrated multi-component coupling packaging equipment, and particularly relates to the technical field of semiconductor laser, the integrated multi-component coupling packaging equipment comprises a base, the top of the base is fixedly connected with a support frame, the top of the base is provided with a control box, and the top of the base is fixedly connected with a fixing frame; the multi-axis positioning system comprises a multi-axis sliding rail fixedly connected to the top of the supporting frame, and the top of the multi-axis sliding rail is slidably connected with a dispensing curing assembly; and a loading assembly. Through the automatic sorting and loading tray, the material conveying system, the automatic robot and the visual monitoring system, the equipment remarkably improves the automation level of the production process, so that the production efficiency is greatly improved, sorting errors of components caused by manual loading are avoided, and the product quality is improved. The conditions of packaging failure and reworking caused by wrong placement of the components are reduced, the labor cost is effectively reduced, the production period is shortened, and the quality and consistency of products are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor laser technology, and in particular to an integrated multi-component coupling packaging device. Background Art

[0002] Semiconductor lasers, also known as laser diodes, have been widely used in many fields due to their high output power, small size, light weight, long life and high photoelectric conversion efficiency. In order to further increase the output power, multiple light-emitting chips are often combined into a linear array, planar array or stacked array structure, and the light beams are focused and coupled into optical fibers through spatial combination to achieve high-power output with high beam quality. The coupling accuracy of each component of the semiconductor laser largely determines the packaging quality of the semiconductor laser. When the coupling accuracy is unqualified, the output power of the semiconductor laser will be significantly reduced.

[0003] In the prior art, manual loading may cause errors in the sorting of components, thereby increasing the occurrence of packaging failures and rework due to incorrect component placement. In the process of coupling components, inaccurate coupling may also easily lead to poor performance. Summary of the invention

[0004] The object of the present invention is to provide an integrated multi-component coupling packaging device to solve the above-mentioned deficiencies in the technology.

[0005] In order to achieve the above-mentioned object, the present invention provides the following technical solution: an integrated multi-component coupling packaging device, comprising: a base, the top of which is fixedly connected to a support frame, a control box is provided on the top of the base, and the top of the base is fixedly connected to a fixing frame; A multi-axis positioning system, the multi-axis positioning system comprising a multi-axis slide rail fixedly connected to the top of the support frame, the top of the multi-axis slide rail being slidably connected to a glue curing component; A charging assembly, the charging assembly comprising a plurality of automatically sorted charging trays fixedly connected to the top of a fixing frame, a material conveying system being provided on the top of the base, an automatic robot being provided on the top of the base, a clamping arm being provided on the top of the automatic robot, a visual monitoring system being provided outside the automatic robot, the visual monitoring system comprising a high-resolution camera for real-time monitoring of the charging process and verifying whether the components are correctly placed through image recognition technology; A force feedback system, including a force sensor mounted on the clamping arm, is used to monitor the clamping force and adjust it to avoid excessive stress or damage to the component; A coupling detection component includes a high-resolution camera fixedly connected to the top of the base, which can capture image details after coupling.

[0006] Preferably, the software algorithm for automatically sorting the loading trays can sort the components according to specific sorting rules, ensuring that each component can be accurately placed in a designated position.

[0007] Preferably, the glue dispensing and curing component includes a glue dispensing path planning algorithm that is required to plan the moving path of the glue dispensing head to ensure that the glue is evenly and accurately applied to the designated area.

[0008] Preferably, the coupling detection component analyzes the image data through a detection algorithm to verify whether the coupling accuracy reaches a preset standard.

[0009] Preferably, the control system in the control box adjusts parameters according to the detection results and can display real-time data and equipment status.

[0010] Preferably, four supporting legs are fixedly connected to the bottom of the base, and buffer pads are provided at the bottoms of the four supporting legs.

[0011] Preferably, the image recognition algorithm in the visual monitoring system is usually based on deep learning, and image data is processed through a convolutional neural network. The convolutional neural network can simulate the way the human brain processes visual information and extract hierarchical features in the image through a multi-layer neural network structure.

[0012] Preferably, the main algorithms involved in the coupling detection component are image processing and machine vision algorithms, which are used to analyze the details of the coupled image captured by a high-resolution camera, including: image preprocessing, feature extraction, image matching and recognition, template matching and feature point matching.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. Through the automatic sorting loading tray, material conveying system, automatic robot, and visual monitoring system, the equipment significantly improves the automation level of the production process, thereby greatly improving production efficiency, avoiding the sorting errors of components caused by manual loading, and reducing the packaging failure and rework caused by incorrect component placement, effectively reducing labor costs, not only shortening the production cycle, but also significantly improving product quality and consistency; 2. Through the coupling detection component, equipped with a high-resolution camera and advanced image processing algorithms, the quality of the coupled product is accurately verified, which not only ensures that the accuracy of the coupling process fully meets the preset quality standards, but also brings an unprecedented level of quality control to the production line. Through real-time monitoring and fine analysis, any slight deviation can be quickly identified and corrected, thereby ensuring the consistency and reliability of the product and further improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a schematic diagram of the overall structure of the present invention; Figure 2 It is a schematic diagram of the structure of the connection between the base and the supporting legs of the present invention.

[0016] Description of reference numerals: 1. Base; 2. Support frame; 3. Control box; 4. Fixed frame; 5. Multi-axis slide rail; 6. Glue dispensing and curing components; 7. Automatic sorting loading tray; 8. Material conveying system; 9. Automatic robot; 10. Clamping arm; 11. Visual monitoring system; 12. High-resolution camera; 13. Support legs. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0018] The present invention provides Figure 1 to Figure 2 An integrated multi-component coupling packaging device is shown, comprising: an integrated multi-component coupling packaging device, characterized in that it comprises: a base 1, a support frame 2 is fixedly connected to the top of the base 1, a control box 3 is provided on the top of the base 1, and a fixing frame 4 is fixedly connected to the top of the base 1; A multi-axis positioning system, the multi-axis positioning system comprises a multi-axis slide rail 5 fixedly connected to the top of the support frame 2, and a glue curing component 6 is slidably connected to the top of the multi-axis slide rail 5; A charging assembly, the charging assembly includes a plurality of automatically sorted charging trays 7 fixedly connected to the top of a fixing frame 4, a material conveying system 8 is provided on the top of the base 1, an automatic robot 9 is provided on the top of the base 1, a clamping arm 10 is provided on the top of the automatic robot 9, and a visual monitoring system 11 is provided outside the automatic robot 9. The visual monitoring system 11 includes a high-resolution camera for real-time monitoring of the charging process and verifying whether the components are correctly placed through image recognition technology; The force feedback system includes a force sensor mounted on the clamping arm 10, which is used to monitor the clamping force and adjust it to avoid excessive pressure or damage to the components; a force sensor is a device that can convert physical force into a measurable signal. When clamping components, the force sensor can monitor the force applied to the components in real time. Through the data collected by the force sensor, the system can accurately control the clamping force to ensure that the force is within the range that the components can withstand. This precise control helps to improve the consistency and reliability of production; Force feedback control algorithm formula: F=k*x Meaning: Where F is the applied force, k is the force constant, and x is the displacement. This formula describes the relationship between force and displacement. Force feedback systems monitor the force applied to components and adjust the clamping force based on a preset force-displacement relationship to avoid damage to components; The coupling detection component includes a high-resolution camera 12 fixedly connected to the top of the base 1, which can capture the image details after coupling.

[0019] The software algorithm of the automatic sorting loading tray 7 can sort the components according to the specific sorting rules, ensuring that each component can be accurately placed in the specified position.

[0020] The glue dispensing and curing component 6 includes a glue dispensing path planning algorithm, which is used to plan the moving path of the glue dispensing head to ensure that the glue is evenly and accurately applied to the specified area, which is achieved by the heuristic search algorithm A*: Point evaluation formula: f(n)=g(n)+h(n) Where f(n) is the total evaluation value of node (n), g(n) is the actual cost from the starting point to node (n), and h(n) is the heuristic estimated cost from node (n) to the target node; The node evaluation formula is used to evaluate the pros and cons of each node, select the node with the smallest total evaluation value as the next path, optimize the dispensing path, and ensure that the glue is applied evenly and accurately in the designated area, thereby improving the consistency and efficiency of dispensing, while avoiding the problem of glue waste and uneven application.

[0021] The coupling detection component analyzes the image data through the detection algorithm to verify whether the coupling accuracy reaches the preset standard. The control system in the control box 3 adjusts the parameters according to the detection results and can display real-time data and equipment status.

[0022] Four supporting legs 13 are fixedly connected to the bottom of the base 1 , and buffer pads are provided at the bottoms of the four supporting legs 13 .

[0023] The image recognition algorithm in the visual monitoring system 11 is usually based on deep learning, and the image data is processed through a convolutional neural network. The convolutional neural network can simulate the way the human brain processes visual information and extract hierarchical features in the image through a multi-layer neural network structure: The purpose of the convolutional layer is to extract features from the image through convolution operations; Convolution operation: ((f*g)(x,y)=sum_{i}sum_{j}f(i,j)g(xi,yj)) Among them, (f) is the input image or feature map, (g) is the convolution kernel (or filter), (*) represents the convolution operation, and ((x,y)) is a point on the output feature map. The convolution operation generates the output feature map by sliding the convolution kernel on the input image and calculating the dot product between the convolution kernel and the local area of ​​the image. This process simulates the receptive field of the visual cortex and can capture local features in the image.

[0024] Activation functions are used to introduce nonlinear factors, enabling neural networks to learn and simulate more complex functions.

[0025] ReLU activation function: (f(x)=max(0,x)) where (x) is the output of the convolutional layer or the fully connected layer. The ReLU function maps negative inputs to 0, while positive inputs remain unchanged. This nonlinear function helps the network learn more complex features while being computationally efficient.

[0026] The pooling layer is used to reduce the dimension of the feature map while retaining important feature information.

[0027] Max pooling: (Pool(x,y)=max(f(x,y))) Among them, (f(x,y)) is the feature map area within the pooling window, (Pool(x,y)) is the output value after pooling, and the maximum pooling operation slides a window on the feature map and selects the maximum value in the window as the output. This helps the network extract the main features in the image and reduces the amount of calculation.

[0028] The fully connected layer connects all activation values ​​of the previous layer to every neuron.

[0029] Neuron output: (y=f(w^Tx+b)) Where (w) is the weight matrix, (x) is the input feature vector, (b) is the bias term, (f) is the activation function, and (y) is the output. Each neuron in the fully connected layer is connected to all activation values ​​in the previous layer, and the output is calculated through weights and biases. This is the key layer in the network for classification and regression tasks.

[0030] The loss function is used to evaluate the difference between the model's predictions and the true values.

[0031] Mean Square Error (MSE): (L=frac{1}{n}sum_{i=1}^{n}(y_i-hat{y}_i)^2) Where (y_i) is the true value, (hat{y}_i) is the predicted value, (n) is the number of samples, and the mean squared error calculates the average of the squares of the differences between the predicted and true values. During training, the weights and biases of the network are adjusted by minimizing the loss function.

[0032] The main algorithms involved in the coupling detection component are image processing and machine vision algorithms, which are used to analyze the details of the post-coupling image captured by the high-resolution camera 12, including: (1) Image preprocessing: improve image quality and facilitate subsequent processing; (2) Feature extraction: Use the Hough transform method to extract key features from the image, such as edges, corners, shapes, etc. Characteristic Hough transform formula: Features In this formula, r is the distance from the origin to the line in polar coordinates, and θ is the angle between the line and the x-axis. The Hough transform converts each point in the image space to a curve or point in the parameter space. In the parameter space, all lines passing through the same point will intersect at one point, and by counting the number of these intersections, we can determine the straight line in the image.

[0033] (3) Image matching and recognition: Match the extracted features with the standard template to determine the coupling accuracy; (4) Template matching: Find the best matching position by calculating the correlation between the template image and the image to be detected;

[0034] In this formula, T is the template image, I is the image to be matched, Tmean and Imean are the average values ​​of the template and image regions, respectively. This formula calculates the correlation between the template and the image region. The higher the value of R(x,y), the better the match between the template and the image at position (x,y).

[0035] (5) Feature point matching: Find stable feature points between two images and calculate the degree of matching between them to evaluate the coupling accuracy.

[0036] Detecting keypoints means finding points in an image with unique features that remain stable under different image transformations. Computing feature descriptors is to create a feature vector for each keypoint that describes the local features of the image area around the keypoint. Feature point matching is to pair feature points in two images and determine whether they correspond to the same physical point by comparing their feature descriptors. This process is used to evaluate coupling accuracy and ensure that the keypoints in the two images can be accurately aligned.

[0037] Working principle: After the equipment is started, the control box 3 initializes all components, including the automatic calibration sensor, the high-resolution camera 12, the clamping arm 10, etc. The operator sets the production parameters. The material conveying system 8 takes out the empty loading tray from the warehouse or directly conveys the components to the loading assembly according to the control system instructions. The clamping arm 10 of the automatic robot 9 places the components on the automatic sorting loading tray 7. The sensors and clamping arm 10 on the automatic sorting loading tray 7 identify the model and direction of the components and sort them according to the set rules. The visual monitoring system 11 monitors the loading process in real time through a high-resolution camera and verifies whether the components are placed correctly through image recognition technology; The automatic robot 9 uses the clamping arm 10 to place the components precisely at the specified position, and then the multi-axis positioning system makes fine adjustments to ensure that the coupling position of the components is accurate. The force feedback system monitors and controls the clamping force to prevent damage to the components.

[0038] The dispensing system applies glue or other adhesive to the coupling part. The glue is then cured to ensure that the component is firmly coupled to the housing. The high-resolution camera 12 in the coupling detection component captures the image details after coupling. The detection algorithm analyzes the image data to verify whether the coupling accuracy meets the preset standards. The control system adjusts the parameters based on the detection results and re-couples if necessary. The control box 3 displays real-time data and equipment status, allowing the operator to monitor the entire process and make necessary interventions. When a component is packaged, the material conveying system 8 removes it and prepares for the packaging of the next component. The control box 3 records production data and generates reports for quality control and process improvement.

[0039] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An integrated multi-component coupling packaging device, characterized in that: include: A base (1), the top of the base (1) being fixedly connected to a support frame (2), the top of the base (1) being provided with a control box (3), and the top of the base (1) being fixedly connected to a fixing frame (4); A multi-axis positioning system, the multi-axis positioning system comprising a multi-axis slide rail (5) fixedly connected to the top of the support frame (2), the top of the multi-axis slide rail (5) being slidably connected to a glue curing component (6); A loading assembly, the loading assembly comprising a plurality of automatically sorted loading trays (7) fixedly connected to the top of a fixed frame (4), a material conveying system (8) being provided on the top of the base (1), an automatic robot (9) being provided on the top of the base (1), a clamping arm (10) being provided on the top of the automatic robot (9), a visual monitoring system (11) being provided outside the automatic robot (9), the visual monitoring system (11) comprising a high-resolution camera for real-time monitoring of the loading process and verifying whether components are correctly placed through image recognition technology; A force feedback system, including a force sensor mounted on the clamping arm (10), for monitoring the clamping force and adjusting it to avoid excessive pressure or damage to the components; A coupling detection component, comprising a high-resolution camera (12) fixedly connected to the top of a base (1) and capable of capturing image details after coupling.

2. The integrated multi-component coupling packaging device according to claim 1, characterized in that: The software algorithm of the automatic sorting loading tray (7) can sort components according to specific sorting rules, ensuring that each component can be accurately placed at a designated position.

3. The integrated multi-component coupling packaging device according to claim 1, characterized in that: The glue dispensing and curing component (6) includes a glue dispensing path planning algorithm that is used to plan the moving path of the glue dispensing head to ensure that the glue is evenly and accurately applied to the designated area.

4. The integrated multi-component coupling packaging device according to claim 1, characterized in that: The coupling detection component analyzes the image data through a detection algorithm to verify whether the coupling accuracy meets the preset standard.

5. The integrated multi-component coupling packaging device according to claim 1, characterized in that: The control system in the control box (3) adjusts parameters according to the detection results and can display real-time data and equipment status.

6. The integrated multi-component coupling packaging device according to claim 1, characterized in that: Four supporting legs (13) are fixedly connected to the bottom of the base (1), and buffer pads are provided at the bottoms of the four supporting legs (13).

7. The integrated multi-component coupling packaging device according to claim 1, characterized in that: The image recognition algorithm in the visual monitoring system (11) is usually based on deep learning and processes image data through a convolutional neural network. The convolutional neural network can simulate the way the human brain processes visual information and extract hierarchical features in the image through a multi-layer neural network structure.

8. The integrated multi-component coupling packaging device according to claim 1, characterized in that: The main algorithms involved in the coupling detection component are image processing and machine vision algorithms, which are used to analyze the details of the coupled image captured by the high-resolution camera (12), including: image preprocessing, feature extraction, image matching and recognition, template matching and feature point matching.

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