Improvement method of laser coding equipment for automobile parts combined with visual positioning
By combining the selection of domestically produced core components, lightweight design, and modular tooling fixtures with AI vision positioning, the problems of high cost, poor flexibility, and inaccurate positioning of existing equipment have been solved, resulting in a high-performance, high-precision laser marking equipment that is suitable for multi-variety, small-batch production and improves the linkage efficiency of the MES system.
Patent Information
- Application Number
- CN202610780005.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-02
- Publication Date
- 2026-08-25
AI Technical Summary
Existing laser marking equipment for automotive parts relies on imported components, resulting in high costs, specialized tooling fixtures, significant impact on positioning accuracy due to placement deviations, and insufficient intelligent integration. It cannot adaptively compensate for part offsets, tilts, and tooling errors, and its linkage with MES traceability systems is inefficient, making it difficult to meet the needs of large-scale production with multiple varieties, small batches, high precision, and low cost.
By selecting and adapting domestically produced core components, using lightweight design, modular quick-change tooling fixtures, and AI visual positioning compensation, combined with machine vision and motion control fusion technology, a highly adaptable and high-precision visual positioning system and laser marking module can be achieved in real time.
It achieves cost reduction through localization, flexible and quick changeover, precise positioning, real-time collaborative coding and full-process traceability, reduces hardware costs, improves equipment flexibility and positioning accuracy, enhances linkage efficiency with MES system, and adapts to the needs of multi-variety and small-batch production.
Smart Images
Figure CN122636725A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent manufacturing technology for automotive parts, specifically to an improved method for laser marking equipment for automotive parts that incorporates visual positioning. Background Technology
[0002] Current laser marking equipment for automotive parts generally suffers from problems such as high cost due to reliance on imported components, specialized tooling and fixtures, slow changeover, significant impact of placement deviations on positioning accuracy, and insufficient intelligent integration. Traditional marking methods are mostly static positioning, unable to adaptively compensate for component offsets, tilts, and tooling errors, and have low efficiency in linkage with MES traceability systems, making it difficult to meet the demands of large-scale production with multiple varieties, small batches, high precision, and low cost. Therefore, developing an improved laser marking method that is cost-effective, flexible, high-precision, and has full-process data traceability capabilities is of great significance. Summary of the Invention
[0003] To address the shortcomings of existing technologies, this invention provides an improved method for laser marking equipment for automotive parts that combines visual positioning. This method solves the problems of high cost, poor flexibility, inaccurate positioning, slow collaboration, and weak traceability in existing technologies, and achieves cost reduction through domestic production, flexible and rapid replacement, precise positioning, real-time collaborative marking, and full-process traceability.
[0004] To achieve the above objectives, the present invention provides the following technical solution: an improved method for laser marking equipment for automotive parts combined with visual positioning, comprising the following steps: S1. Establish a benchmark library for the performance of imported and domestic components, select and adapt core components for laser marking equipment to localization, and complete the integration testing and communication protocol adaptation of domestic lasers, galvanometers, industrial control systems and vision cameras. S2. Lightweight and vibration-resistant design of equipment frame, tooling and vision installation structure. Optimize structural rigidity through finite element simulation to reduce the impact of equipment vibration on vision positioning; S3. Modular quick-change tooling fixtures are used to clamp and fix multi-specification parts, and rapid changeover is achieved through standardized interfaces. AI vision positioning is used to compensate for fixture positioning deviations. S4. Based on the fusion technology of machine vision and motion control, develop a highly adaptable and high-precision visual positioning system to achieve real-time collaboration with the laser marking module.
[0005] Preferably, the content of step S1 is as follows: For core components such as imported lasers, galvanometers, industrial control systems, sensors, AI vision cameras, and image acquisition cards, we conduct performance benchmarking analysis to select domestically produced components with equivalent technical parameters. We then adapt and develop electrical interfaces, communication protocols, and control logic in conjunction with the overall equipment architecture to ensure the compatibility of domestically produced components with the equipment system and guarantee the collaborative response accuracy of the AI vision system and the coding module.
[0006] Preferably, the content of step S2 is as follows: Topology optimization and finite element analysis methods were used to perform mechanical simulation on the core mechanical structures of the equipment frame, coding work platform, and AI vision camera mounting bracket. Redundant structures were removed, and high-strength, lightweight alloy materials were selected to replace traditional heavy steel materials to reduce the impact of equipment operation vibration on visual positioning accuracy.
[0007] Preferably, step S3 involves: adopting a modular design concept, disassembling the fixture into a basic support module, a positioning module, and a clamping module, and adapting to automotive parts of different sizes and shapes by replacing different positioning and clamping components; at the same time, designing a quick-change interface to achieve rapid disassembly and assembly of the fixture components, and using AI visual positioning algorithms to accurately identify the actual position of the parts, compensating for minor positioning deviations after fixture replacement.
[0008] Preferably, step S4 involves: acquiring surface images of automotive parts using an industrial camera; performing image preprocessing; identifying feature targets of the parts using adaptive threshold segmentation and contour extraction algorithms; calculating the deviation between the actual placement position of the parts and the reference position using a multi-target coordinate calibration model; transmitting the deviation data to the coding control module in real time; dynamically adjusting the galvanometer scanning path; and achieving closed-loop control of positioning and coding.
[0009] Preferably, the performance benchmarking analysis includes laser power stability, galvanometer response speed, AI vision camera frame rate, and image acquisition card transmission bandwidth.
[0010] Preferably, the preprocessing of images of automotive parts surface acquired by industrial cameras includes denoising, enhancement, and correction.
[0011] This invention provides an improved method for laser marking equipment for automotive parts that incorporates visual positioning. It offers the following advantages: 1. Break through the compatibility restrictions of imported core components, realize the localization and standardization of core configurations, significantly reduce hardware procurement costs, and ensure that coding performance is on par with imported configurations.
[0012] 2. The development of cross-scenario tooling fixture universal adaptation technology and AI visual adaptive positioning algorithm is an innovative integration that breaks the limitation of traditional equipment that can only adapt to a single tooling fixture. By combining modular tooling fixture design with AI visual positioning algorithm, the equipment is compatible with multiple mainstream tooling fixtures. Visual positioning is used to compensate for fixture changeover errors, eliminating the complicated fixture adaptation and debugging process, reducing the inventory of idle fixtures, and enabling rapid changeover on the production line.
[0013] 3. Develop dynamic coding path adjustment technology based on real-time positioning data. By dynamically monitoring the position of parts through an AI vision system, the coding coordinates are corrected in real time, solving the accuracy deviation problem caused by the micro-movement of parts in traditional static positioning coding and improving the coding quality under complex working conditions. Attached Figure Description
[0014] Figure 1 This is a schematic diagram illustrating the process of precise feature recognition of automotive parts using the improved method of combining visual positioning with laser marking equipment for automotive parts proposed in this invention. Detailed Implementation
[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example
[0016] This invention provides an improved method for laser marking equipment for automotive parts that incorporates visual positioning, comprising the following steps: S1. Establish a benchmark library for the performance of imported and domestic components, select and adapt core components for laser marking equipment to localization, and complete the integration testing and communication protocol adaptation of domestic lasers, galvanometers, industrial control systems and vision cameras. S2. Lightweight and vibration-resistant design of equipment frame, tooling and vision installation structure. Optimize structural rigidity through finite element simulation to reduce the impact of equipment vibration on vision positioning; S3. Modular quick-change tooling fixtures are used to clamp and fix multi-specification parts, and rapid changeover is achieved through standardized interfaces. AI vision positioning is used to compensate for fixture positioning deviations. S4. Based on the fusion technology of machine vision and motion control, develop a highly adaptable and high-precision visual positioning system to achieve real-time collaboration with the laser marking module.
[0017] Step S1 involves: for core components such as imported lasers, galvanometers, industrial control systems, sensors, AI vision cameras, and image acquisition cards, through performance benchmarking analysis, selecting domestically produced components with equivalent technical parameters, and adapting and developing electrical interfaces, communication protocols, and control logic in conjunction with the overall equipment architecture to ensure the compatibility of domestically produced components with the equipment system and to ensure the collaborative response accuracy of the AI vision system and the coding module.
[0018] The specific technical details are as follows: Establish a benchmark library of performance parameters for imported and domestically produced components, covering key indicators such as laser power stability, galvanometer response speed, AI vision camera frame rate, and image acquisition card transmission bandwidth; build a component testing platform to conduct continuous coding durability tests on domestically produced lasers, galvanometer positioning accuracy tests, industrial control system computing efficiency tests, and AI vision camera image acquisition clarity and dynamic capture capability tests; and optimize circuit design, software drivers, and vision-coding collaborative logic to address issues such as signal interference, data transmission stuttering, and visual positioning delays encountered during the adaptation process.
[0019] Step S2 involves using topology optimization and finite element analysis to perform mechanical simulation on the core mechanical structures of the equipment frame, coding platform, and AI vision camera mounting bracket, removing redundant structures, and replacing traditional heavy steel with high-strength, lightweight alloy materials to reduce the impact of equipment vibration on visual positioning accuracy.
[0020] The specific technical details are as follows: Lightweight design of the equipment structure is completed using 3D modeling software; the load-bearing and vibration resistance performance of the structure is verified through finite element analysis to ensure that the vibration displacement of the AI vision camera mounting surface is ≤0.005mm; the processing technology of the parts is optimized, and integrated die casting and sheet metal bending processes are adopted to reduce assembly steps, improve structural compactness, and shorten the signal transmission distance between the AI vision system and the dock; the lightweight mechanical structure is field-tested to verify its stability under long-term working conditions and ensure the consistency of AI vision positioning.
[0021] Step S3 involves adopting a modular design concept, disassembling the fixture into a basic support module, a positioning module, and a clamping module. By replacing different positioning and clamping components, it can be adapted to automotive parts of different sizes and shapes. At the same time, a quick-change interface is designed to enable the quick assembly and disassembly of the fixture components. Combined with AI visual positioning algorithms, it can accurately identify the actual position of the parts and compensate for minor positioning deviations after fixture replacement.
[0022] The specific technical details are as follows: The dimensions and shape characteristics of automotive parts are analyzed, and standardized interface specifications for the fixture module are established to ensure the consistency of the fixture reference surface after model change; wear-resistant and high-strength engineering plastics and alloy materials are selected to make fixture components, taking into account both lightweight and durability, and avoiding damage to the surface of parts; the clamping stability of the fixture is tested, and the displacement deviation of parts during high-speed coding is verified by combining an AI vision positioning system, and the positioning accuracy is closed-loop controlled through algorithm compensation.
[0023] Step S4 involves: acquiring surface images of automotive parts using an industrial camera; after image preprocessing, identifying feature targets of the parts using adaptive threshold segmentation and contour extraction algorithms; calculating the deviation between the actual placement position of the parts and the reference position using a multi-target coordinate calibration model; transmitting the deviation data to the marking control module in real time; dynamically adjusting the galvanometer scanning path; and achieving closed-loop control of positioning and marking.
[0024] The specific technical details are as follows: An AI visual positioning testing platform is built, integrating domestically produced industrial cameras, lenses, light sources, and image acquisition cards. Image acquisition tests are conducted on components made of different materials and with different surface conditions, such as steel, aluminum, and copper. The angle and intensity of the light source are optimized to reduce the impact of surface reflection on image quality. Please refer to... Figure 1 An adaptive contour extraction algorithm was developed, which trains a feature template library of different parts through machine learning to achieve accurate feature recognition of irregular and non-standard parts with an accuracy rate of ≥99.5%. The vision-coding collaborative control logic was optimized, and an interrupt triggering mechanism was adopted to realize real-time transmission of positioning data and dynamic adjustment of coding parameters, controlling the positioning-coding response delay to ≤20ms. Multi-condition tests were conducted to verify that the visual positioning accuracy is ≤±0.01mm when the part placement offset is ≤±5mm and the tilt angle is ≤3°.
[0025] Preprocessing of images of automotive parts surfaces acquired by industrial cameras includes denoising, enhancement, and correction.
[0026] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. An improved method for laser marking equipment for automotive parts combining visual positioning, characterized in that: Includes the following steps: S1. Establish a benchmark library for the performance of imported and domestic components, select and adapt core components for laser marking equipment to localization, and complete the integration testing and communication protocol adaptation of domestic lasers, galvanometers, industrial control systems and vision cameras. S2. Lightweight and vibration-resistant design of equipment frame, tooling and vision installation structure. Optimize structural rigidity through finite element simulation to reduce the impact of equipment vibration on vision positioning; S3. Modular quick-change tooling fixtures are used to clamp and fix multi-specification parts, and rapid changeover is achieved through standardized interfaces. AI vision positioning is used to compensate for fixture positioning deviations. S4. Based on the fusion technology of machine vision and motion control, develop a highly adaptable and high-precision visual positioning system to achieve real-time collaboration with the laser marking module.
2. The improved method of the automotive parts laser marking equipment combined with visual positioning according to claim 1, characterized in that: The content of step S1 is as follows: For core components such as imported lasers, galvanometers, industrial control systems, sensors, AI vision cameras, and image acquisition cards, we conduct performance benchmarking analysis to select domestically produced components with equivalent technical parameters. We then adapt and develop electrical interfaces, communication protocols, and control logic in conjunction with the overall equipment architecture to ensure the compatibility of domestically produced components with the equipment system and guarantee the collaborative response accuracy of the AI vision system and the coding module.
3. The improved method of the automotive parts laser marking equipment combined with visual positioning according to claim 1, characterized in that: The content of step S2 is as follows: Topology optimization and finite element analysis methods were used to perform mechanical simulation on the core mechanical structures of the equipment frame, coding work platform, and AI vision camera mounting bracket. Redundant structures were removed, and high-strength, lightweight alloy materials were selected to replace traditional heavy steel materials to reduce the impact of equipment operation vibration on visual positioning accuracy.
4. The improved method of the automotive parts laser marking equipment combined with visual positioning according to claim 1, characterized in that: The content of step S3 is as follows: adopting a modular design concept, the fixture is disassembled into a basic support module, a positioning module, and a clamping module. By replacing different positioning and clamping components, it can be adapted to automotive parts of different sizes and shapes. At the same time, a quick-change interface is designed to realize the quick disassembly and assembly of the fixture components. Combined with the AI visual positioning algorithm, the actual position of the parts is accurately identified to compensate for the slight positioning deviation after the fixture is changed.
5. The improved method of the automotive parts laser marking equipment combined with visual positioning according to claim 1, characterized in that: The content of step S4 is as follows: an industrial camera is used to acquire images of the surface of automotive parts. After image preprocessing, an adaptive threshold segmentation and contour extraction algorithm is used to identify the feature target points of the parts. The deviation between the actual placement position of the parts and the reference position is calculated by combining the multi-target point coordinate calibration model. The deviation data is transmitted to the coding control module in real time, and the galvanometer scanning path is dynamically adjusted to realize the closed-loop control of positioning and coding.
6. The improved method of the automotive parts laser marking equipment combined with visual positioning according to claim 2, characterized in that: The performance benchmarking analysis includes laser power stability, galvanometer response speed, AI vision camera frame rate, and image acquisition card transmission bandwidth.
7. The improved method of the automotive parts laser marking equipment combined with visual positioning according to claim 5, characterized in that: Preprocessing of images of automotive parts surfaces acquired by industrial cameras includes denoising, enhancement, and correction.