Intelligent dispensing program planning method for dispensing system

Abstract

The invention discloses an intelligent dispensing program planning method for a dispensing system. The method comprises the following steps: identifying an imported product drawing and generating a drawing information graph; establishing an element dispensing library; a dispensing coordinate graph is generated according to the drawing information graph and the element dispensing library; establishing a motion data large model; generating a path planning graph according to the dispensing coordinate graph and the motion data large model; a dispensing track diagram is generated in combination with a path optimization algorithm; a dispensing program is generated according to the dispensing track diagram; the method comprises the following steps: identifying an imported product drawing, generating a drawing information graph, generating a dispensing coordinate graph according to the drawing information graph and an element dispensing library, generating a path planning graph through a motion data large model, and performing path optimization in combination with a path optimization algorithm to generate a dispensing track graph. Therefore, the dispensing path with the shortest motion path and the highest dispensing efficiency is obtained, dispensing program editing of complex products can be rapidly, accurately and efficiently carried out, and the problems that an existing program editing mode is low in efficiency and quality are solved.

Description

Technical Field

[0001] The invention relates to the technical field of glue dispensing, and in particular to a method for intelligently planning a glue dispensing program for a glue dispensing system. Background Art

[0002] In the field of dispensing industry, it is essential to edit the dispensing machine program for products that require dispensing. In the past, dispensing products were relatively simple, with relatively few dispensing tracks, and the requirements for equipment operating technicians were relatively low. However, with the development of science and technology, more and more products require dispensing, especially in the fields of automotive, military, and aerospace. The products that require dispensing are getting larger and larger, and the dispensing tracks are increasing. For the editing of thousands of dispensing positions, if it is done manually, it will take a lot of time and the accuracy rate is not high. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention aims to provide a method for intelligently planning a dispensing program for a dispensing system to solve the problems raised in the above background technology. To achieve the above purpose, the present invention adopts the following technical solutions:

[0004] The method for intelligently planning a dispensing program of a dispensing system includes the following steps: identifying an imported product drawing and generating a drawing information graphic;

[0005] Establish component dispensing library, including the name, type, dispensing quantity, glue amount and instruction information of each component, which is used to define each component in the drawing information graphics;

[0006] Generate a glue dispensing coordinate diagram according to the drawing information graphics and the component glue dispensing library;

[0007] Collect motion information data of dispensing equipment and establish a large motion data model;

[0008] Generate a path planning diagram based on the dispensing coordinate diagram and the motion data large model;

[0009] Optimize the path according to the path planning diagram and the path optimization algorithm to generate a dispensing trajectory diagram;

[0010] A dispensing program is generated according to the dispensing trajectory diagram.

[0011] Optionally, the identifying and generating the drawing information graphics from the imported product drawings comprises: reading an information block and a data block in the product drawings, wherein the information block comprises a drawing size and a measurement unit, and the data block comprises coordinate data, a function code and a terminator of each component;

[0012] Generate graphic elements of each component according to the coordinate data and the function code;

[0013] The drawing information graphic is generated according to the drawing size, the measurement unit and the dispensing graphic element.

[0014] Optionally, a dispensing coordinate diagram is generated based on the drawing information graphics and the component dispensing library, including: identifying the dispensing components based on the characteristics of the graphic elements; matching with each component in the component dispensing library; and generating a dispensing coordinate diagram for each dispensing component based on the information block.

[0015] Optionally, the collection of motion information data of the dispensing equipment includes: simulating various dispensing motion scenes, collecting motor parameters, motion speed range and acceleration characteristics of the dispensing equipment, and calculating the stabilization time of the dispensing equipment under various motion speeds or acceleration conditions.

[0016] Optionally, the motion data large model includes: an input layer, a hidden layer and an output layer, the input layer is used to input the motion information data, environmental data and process parameters of the dispensing equipment; the hidden layer integrates and models the motion information data, environmental monitoring data and process parameters of the dispensing equipment in the input layer through its internal neurons and connection weights, and constructs a more complex functional relationship to fit the data, and accurately predicts different dispensing paths and equipment states through continuous learning, parameter adjustment and training; the output layer is used to output the path prediction results of the hidden layer.

[0017] Optionally, the path optimization algorithm includes: the shortest path principle, the horizontal or vertical priority and the shortest path principle, the set angle direction priority and the shortest path principle.

[0018] Optionally, generating a path planning map based on the dispensing coordinate map and the motion data large model includes: utilizing the precise coordinate information provided in the dispensing coordinate map; analyzing the motion accuracy of the device in combination with the motion data large model; predicting the actual positioning error of the device under different motion parameters, and compensating for it during path planning to generate a path planning map.

[0019] Optionally, the shortest path principle includes: searching for the path point that is closest to the current position and has not been visited within a certain range, and moving in a straight line to the nearest path point until all path points are traversed to form a complete and relatively short dispensing path.

[0020] Optionally, the horizontal or vertical priority and shortest path principle includes: searching horizontally or vertically for the path point that is closest to the current position and has not been visited within a certain field of view, and moving in a straight line to the nearest path point until all path points are traversed to form a complete and relatively short dispensing path.

[0021] Optionally, the setting angle direction priority and shortest path principle includes: searching for the closest and unvisited path point to the current position along a fixed angle direction within a certain range, and moving in a straight line to the nearest path point until all path points are traversed to form a complete and relatively short dispensing path.

[0022] Compared with the prior art, the beneficial effect of the present invention is that, by adopting the above scheme, the present invention identifies the imported product drawings and generates drawing information graphics, generates a dispensing coordinate diagram according to the drawing information graphics and the component dispensing library, generates a path planning diagram through a large motion data model, and generates a dispensing trajectory diagram by combining the path optimization algorithm to optimize the path, thereby obtaining a dispensing path with the shortest motion path and the highest dispensing efficiency, and generating a dispensing program; the intelligent dispensing planning method can quickly, accurately and efficiently edit the dispensing program of complex products, making up for the low efficiency and low quality of the existing program editing method; at the same time, it can realize the advantages of offline programming and maximize the performance of the machine, thereby improving equipment efficiency and quality, and improving the intelligence of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The present invention is a schematic flow chart of a method for intelligently planning a dispensing program for a dispensing system. DETAILED DESCRIPTION

[0024] To facilitate the understanding of the present invention, the present application is described in more detail below in conjunction with the accompanying drawings and specific embodiments; the accompanying drawings show preferred embodiments of the present application; however, the present application can be implemented in many different forms and is not limited to the embodiments described in this specification; on the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive.

[0025] It should be noted that, unless otherwise clearly specified and limited, the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence; it should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0026] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by technicians in the technical field to which this application belongs. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not used to limit this application.

[0027] like Figure 1 As shown, one embodiment of the present invention is: the method for intelligently planning a dispensing program of a dispensing system comprises the following steps: identifying an imported product drawing and generating a drawing information graphic;

[0028] Establish component dispensing library, including the name, type, dispensing quantity, glue amount and instruction information of each component, which is used to define each component in the drawing information graphics;

[0029] Generate a glue dispensing coordinate diagram according to the drawing information graphics and the component glue dispensing library;

[0030] Collect motion information data of dispensing equipment and establish a large motion data model;

[0031] Generate a path planning diagram based on the dispensing coordinate diagram and the motion data large model;

[0032] Optimize the path according to the path planning diagram and the path optimization algorithm to generate a dispensing trajectory diagram;

[0033] A dispensing program is generated according to the dispensing trajectory diagram.

[0034] In one embodiment, the step of identifying the imported product drawings and generating the drawing information graphics includes: reading an information block and a data block in the product drawings, wherein the information block includes the drawing size and the measurement unit, and the data block includes the coordinate data, the function code and the terminator of each component;

[0035] Generate graphic elements of each component according to the coordinate data and the function code;

[0036] The drawing information graphic is generated according to the drawing size, the measurement unit and the dispensing graphic element.

[0037] It can be understood that the drawing size information specifies the actual physical size of the dispensing product, which is given in the form of a coordinate range. The unit information determines the measurement unit of the subsequent coordinate data, which is essential for accurate graphics drawing and positioning. Coordinate data is key information for defining the position and shape of each component on the dispensing product. Function codes include D code, G code, M code, etc. D code is mainly used to define the size attributes of graphic elements, such as line width, pad diameter, etc. G code controls the drawing mode and operation of the graphic, and M code is used to represent some auxiliary functions and processing operations. Based on the parsed coordinate data and function codes, the software gradually constructs various graphic elements on the dispensing product, and generates drawing information graphics including graphic elements of each component on the entire dispensing product according to the drawing size, measurement unit and dispensing graphic elements.

[0038] In one embodiment, a dispensing coordinate diagram is generated based on the drawing information graphics and the component dispensing library, including: identifying the dispensing components based on the characteristics of the graphic elements; matching with each component in the component dispensing library; and generating a dispensing coordinate diagram for each dispensing component based on the information block.

[0039] It can be understood that the dispensing components are identified by analyzing the shape, size, positional relationship and other features of the graphic elements in the drawing information graphics, and matched with the records in the component library. According to the dispensing quantity and positional relationship of the components, the coordinates and offset of each dispensing position relative to the reference point are calculated, and the calculated dispensing positions are drawn on a two-dimensional plane.

[0040] The dispensing component library stores detailed information of various dispensing components in a structured way. For each component, the library contains information such as the component name, type, size specification, dispensing quantity, amount of glue required, dispensing instructions, etc. This information can be stored in the form of a database table, where each field corresponds to a specific attribute.

[0041] In one embodiment, in order to quickly and accurately find information matching the dispensing component identified in the drawing from the component library, the component library establishes an effective indexing and retrieval mechanism. Usually, an index can be established based on the type, name or some key attributes of the component, so that the record of the relevant component can be quickly located when calling.

[0042] In one embodiment, the collection of motion information data of the dispensing equipment includes: simulating various dispensing motion scenes, collecting motor parameters, motion speed range and acceleration characteristics of the dispensing equipment, and calculating the stabilization time of the dispensing equipment under various motion speeds or acceleration conditions.

[0043] It is understandable that key kinematic features such as displacement, velocity, acceleration, jerk, etc. are extracted from the collected motion information data. The displacement feature can be directly obtained from the data of the linear displacement sensor, the velocity feature is obtained by calculating the first-order differential of the displacement data, the acceleration feature is calculated by the first-order differential of the velocity data or the second-order differential of the displacement data, and the jerk feature is calculated by the first-order differential of the acceleration data. These kinematic features can fully describe the motion state and motion characteristics of the dispensing equipment, providing an important basis for subsequent model construction and analysis.

[0044] It also includes calculating the curvature, torsion and other geometric features of the motion trajectory; for complex dispensing trajectories, such as curves or arcs, curvature and torsion can reflect the degree of bending and twisting of the trajectory. The curvature can be calculated by deriving the mathematical formula of the trajectory curve, or by using numerical calculation methods, such as approximate calculation methods based on discrete points. The calculation of torsion requires comprehensive consideration of the changes in the trajectory in three-dimensional space. These geometric features are of great significance for optimizing the dispensing path and avoiding collisions.

[0045] In one embodiment, the motion data large model includes: an input layer, a hidden layer and an output layer. The input layer is used to input the motion information data, environmental data and process parameters of the dispensing equipment. It is of great significance for predicting the future motion trajectory of the equipment. In addition to these basic characteristics, other relevant factors can also be considered, such as acceleration (rate of change of acceleration), which is more detailed in describing the smoothness and dynamic characteristics of the equipment movement; and the load conditions during equipment operation, because load changes may affect the movement performance of the equipment. The input layer can select multiple node numbers, and the number of nodes should correspond to the number of selected input features one by one to ensure that each feature has an independent input channel to transmit information to the neural network of the motion data large model.

[0046] The hidden layer integrates and models the motion information data, environmental monitoring data and process parameters of the input layer's dispensing equipment through its internal neurons and connection weights, constructs a more complex functional relationship to fit the data, and accurately predicts different dispensing paths and equipment states through continuous learning, parameter adjustment and training; the output layer is used to output the path prediction results of the hidden layer.

[0047] The hidden layer may include multiple layers, each layer having a different number of nodes. The number of layers and the number of nodes can enhance the expressive power of the neural network, enabling it to fit more complex nonlinear relationships.

[0048] The output layer includes multiple nodes, each of which is responsible for outputting specific values ​​related to the predicted target. The predicted position coordinates need to take into account the motion accuracy of the equipment and the size limitations of the workpiece to determine whether the predicted position is within a reasonable range. If the next position coordinate is predicted, the control system of the dispensing equipment can adjust the movement direction and speed in advance based on these predicted values ​​to achieve more accurate dispensing operations.

[0049] It is understandable that the motion data model can accurately predict the motion trajectory of the dispensing equipment under different working conditions by learning a large amount of historical motion information data. The model can calculate the optimal motion path in advance based on the current dispensing task parameters (such as dispensing graphics, dispensing speed requirements, etc.) and the real-time status of the equipment (such as motor speed, position of moving parts, etc.), thereby greatly improving the accuracy of dispensing path planning.

[0050] In one embodiment, the path optimization algorithm includes: the shortest path principle, the horizontal or vertical priority and the shortest path principle, and the set angle direction priority and the shortest path principle.

[0051] It can be understood that the path optimization algorithm is based on graph theory and combinatorial optimization theory. It evaluates the weights or costs of different paths and adopts strategies such as search and dynamic programming to find the optimal solution.

[0052] In one embodiment, the path planning map is generated based on the dispensing coordinate map and the motion data large model, including: utilizing the precise coordinate information provided in the dispensing coordinate map; analyzing the motion accuracy of the device in combination with the motion data large model; predicting the actual positioning error of the device under different motion parameters, and compensating for it during path planning to generate a path planning map.

[0053] It is understandable that the motion data big model can predict the optimal motion speed of the device in different path segments based on the historical motion data and current status (such as motor load, temperature, etc.) of the dispensing device, and use the motion data big model to analyze the motion trajectory of the device, smooth the planned path, accurately draw the dispensing position in the graph according to the coordinate information in the dispensing coordinate diagram, and connect each dispensing position with lines in the order of path planning to form an intuitive path planning diagram with path trajectory. Different colors, line types (such as solid lines, dotted lines, etc.) and line widths can also be set for the path trajectory to clearly distinguish different path segments or indicate the importance of the path.

[0054] In one embodiment, the shortest path principle includes: searching for the path point that is closest to the current position and has not been visited within a certain range, and moving in a straight line to the nearest path point until all path points are traversed to form a complete and relatively short dispensing path.

[0055] It is understandable that the shortest path principle adopts the snake algorithm. In the path planning scenario, it starts from the starting point and selects the point closest to the current point and has not been visited as the next target point each time, just like the snake always moves towards the food closest to it when looking for food. For example, in a two-dimensional dispensing coordinate diagram, there are multiple dispensing positions (target points). The algorithm starts from the starting dispensing position, calculates the distance between it and all unvisited dispensing positions, and then selects the closest point as the next point to go to. This distance can be calculated using the Euclidean distance formula To calculate, (X1, Y1) is the coordinate of the current point, (X2, Y2) is the coordinate of the target point. In this way, the algorithm gradually builds a path from the starting point to other dispensing locations.

[0056] In one embodiment, the horizontal or vertical priority and shortest path principle includes: searching horizontally or vertically for the path point that is closest to the current position and has not been visited within a certain field of view, and moving in a straight line to the nearest path point until all path points are traversed to form a complete and relatively short dispensing path; on the basis of the snake algorithm, a horizontal or vertical priority algorithm is added. This method is mostly used in applications to improve flight dispensing efficiency.

[0057] In one embodiment, the angle direction priority and shortest path principle are set, including: searching for the path point that is closest to the current position and has not been visited within a certain range along a fixed angle direction, and moving in a straight line to the nearest path point until all path points are traversed to form a complete and relatively short dispensing path; based on the snake algorithm, the angle direction priority principle is added. This method is mostly used to improve the efficiency of flying dispensing of tilted products.

[0058] The present invention identifies imported product drawings and generates drawing information graphics, generates a glue dispensing coordinate diagram according to the drawing information graphics and the component glue dispensing library, generates a path planning diagram through a large motion data model, and generates a glue dispensing trajectory diagram by combining a path optimization algorithm to perform path optimization, thereby obtaining a glue dispensing path with the shortest motion path and the highest glue dispensing efficiency, and generating a glue dispensing program; the intelligent glue dispensing planning method can quickly, accurately and efficiently edit the glue dispensing program of complex products, making up for the problems of low efficiency and low quality of existing program editing methods; at the same time, it can realize the advantages of offline programming and giving full play to the maximum performance of the machine, thereby improving equipment efficiency and quality, and improving the intelligence level of the equipment.

[0059] It should be noted that the above-mentioned technical features are further combined with each other to form various embodiments not listed above, which are all deemed to be within the scope of the present invention; and, for ordinary technicians in this field, they can be improved or transformed according to the above description, and all these improvements and transformations should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for intelligently planning a dispensing program for a dispensing system, characterized in that: The following steps are involved: Identify imported product drawings and generate drawing information graphics; Establish component dispensing library, including the name, type, dispensing quantity, glue amount and instruction information of each component, which is used to define each component in the drawing information graphics; Generate a glue dispensing coordinate diagram according to the drawing information graphics and the component glue dispensing library; Collect motion information data of dispensing equipment and establish a large motion data model; Generate a path planning diagram based on the dispensing coordinate diagram and the motion data large model; Optimize the path according to the path planning diagram and the path optimization algorithm to generate a dispensing trajectory diagram; A dispensing program is generated according to the dispensing trajectory diagram.

2. According to claim 1, a method for intelligently planning a dispensing program for a dispensing system is characterized in that: The step of identifying the imported product drawings and generating the drawing information graphics includes: reading the information block and the data block in the product drawings, wherein the information block includes the drawing size and the measurement unit, and the data block includes the coordinate data, the function code and the terminator of each component; Generate graphic elements of each component according to the coordinate data and the function code; The drawing information graphic is generated according to the drawing size, the measurement unit and the dispensing graphic element.

3. A method for intelligently planning a dispensing program for a dispensing system according to claim 2, characterized in that: Generate a dispensing coordinate diagram according to the drawing information graphics and the component dispensing library, including: identifying the dispensing components according to the characteristics of the graphic elements; matching with each component in the component dispensing library; and generating a dispensing coordinate diagram of each dispensing component according to the information block.

4. The method for intelligently planning a dispensing program for a dispensing system according to claim 1, characterized in that: The collection of motion information data of the dispensing equipment includes: simulating various dispensing motion scenes, collecting motor parameters, motion speed range and acceleration characteristics of the dispensing equipment, and calculating the stabilization time of the dispensing equipment under various motion speeds or acceleration conditions.

5. A method for intelligently planning a dispensing program for a dispensing system according to claim 4, characterized in that: The motion data large model includes: an input layer, a hidden layer and an output layer. The input layer is used to input the motion information data, environmental data and process parameters of the dispensing equipment; the hidden layer integrates and models the motion information data, environmental monitoring data and process parameters of the dispensing equipment in the input layer through its internal neurons and connection weights, and constructs a more complex functional relationship to fit the data. Through continuous learning, parameter adjustment and training, different dispensing paths and equipment states are accurately predicted; the output layer is used to output the path prediction results of the hidden layer.

6. The method for intelligently planning a dispensing program for a dispensing system according to claim 1, characterized in that: The path optimization algorithm includes: the shortest path principle, the horizontal or vertical priority and the shortest path principle, and the set angle direction priority and the shortest path principle.

7. A method for intelligently planning a dispensing program for a dispensing system according to claim 6, characterized in that: The method of generating a path planning map based on the dispensing coordinate map and the motion data large model includes: utilizing the precise coordinate information provided in the dispensing coordinate map; analyzing the motion accuracy of the equipment in combination with the motion data large model; predicting the actual positioning error of the equipment under different motion parameters, and compensating for it during path planning to generate a path planning map.

8. The method for intelligently planning a dispensing program for a dispensing system according to claim 6, characterized in that: The shortest path principle includes: searching for the path point that is closest to the current position and has not been visited within a certain range, and moving in a straight line to the nearest path point until all path points are traversed to form a complete and relatively short dispensing path.

9. The method for intelligently planning a dispensing program for a dispensing system according to claim 6, characterized in that: The principle of horizontal or vertical priority and shortest path includes: searching horizontally or vertically for the path point that is closest to the current position and has not been visited within a certain field of view, and moving in a straight line to the nearest path point until all path points are traversed to form a complete and relatively short dispensing path.

10. The method for intelligently planning a dispensing program for a dispensing system according to claim 6, characterized in that: The principle of setting angle direction priority and shortest path includes: searching for the path point closest to the current position and not visited within a certain range along a fixed angle direction, and moving in a straight line to the nearest path point until all path points are traversed to form a complete and relatively short dispensing path.

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