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　　In modern manufacturing, welding technology is a critical factor in ensuring product quality and reliability. Particularly in the production of transformers, the precision and consistency of welding are crucial to product performance and longevity. Traditional welding methods have certain limitations, and with technological advancements, laser seam tracking systems have emerged, offering new solutions to these challenges. This article explores the application of the ATINY laser seam tracking system with multipoint positioning in automatic transformer welding. 　　Principle of Laser Seam Tracking 　　The ATINY laser seam tracking system uses advanced laser vision technology, employing high-definition cameras to capture real-time information about the seam's position and shape. High-speed data processing algorithms accurately calculate the welding path. The system can extract characteristic parameters of the seam, such as position, shape, and width, in real time, guiding the robot for precise welding operations. Additionally, the system has strong anti-interference capabilities, enabling it to operate stably in complex welding environments. 　　The real-time tracking functionality of the ATINY laser seam tracking system addresses issues like welding heat distortion, material deviation, and clamping-induced deviation, ensuring stability and precision in the welding process. 　　Challenges in Automatic Transformer Welding 　　As a core component of power systems, transformers require extremely high welding quality. In transformer production, welding processes are mainly concentrated on the connections of the core, windings, and oil tank, which are complex and variable, presenting the following challenges: 　　Complex Seam Shapes: The transformer structure is complex, and the seam shapes are diverse, making traditional welding methods difficult to adapt. 　　High Precision Requirements: The compact internal structure of transformers demands extremely high seam position accuracy, where even minor deviations can affect product performance. 　　Consistency Issues: Automatic welding equipment can lead to seam position inconsistencies due to fixture problems. 　　ATINY Sea

　　Molten pool monitoring cameras play a crucial role in modern welding technology. With the continuous advancement of industrial automation and intelligent manufacturing, improving welding quality and efficiency has become a focal point of the industry. Molten pool monitoring cameras provide strong support for enhancing welding quality and optimizing welding processes by real-time monitoring and analyzing the dynamics of the molten pool. This article, in collaboration with Chuangxiang Zhikong, explores the principles and applications of molten pool monitoring cameras. 　　I. Basic Principles of Molten Pool Monitoring Camera 　　Molten pool monitoring cameras primarily monitor welding quality by capturing and analyzing images of the molten pool during the welding process. The working principle can be divided into the following steps: 　　Image Capture: 　　The molten pool monitoring camera is installed on the welding equipment to capture real-time images of the molten pool during the welding process. These images contain key information such as the shape and size of the molten pool, reflecting the actual welding condition. 　　Image Processing: 　　The captured images are processed by a high-speed image processing system. The processing includes steps such as noise reduction, edge detection, and feature extraction. These processes clearly identify the contour and features of the molten pool while filtering out interference. 　　Data Analysis: 　　The processed image data is sent to a data analysis system. The system evaluates welding quality by analyzing the molten pool’s shape. For example, the uniformity of the molten pool’s shape reflects the welding quality and stability. 　　Feedback Control: 　　Some advanced molten pool monitoring systems have real-time feedback control functionality. Based on the image analysis results, the system can automatically adjust welding parameters (such as welding current, voltage, welding speed, etc.) to optimize the welding process. This closed-loop control significantly improves welding precision and consistency. 　　Storage and Recording: 　　Molten pool monitoring systems usually record images and data from the weld

　　With the continuous development of the manufacturing industry, automated welding technology is increasingly being applied across various sectors. As a critical security device, the welding quality in the production of safes directly affects the product's safety and reliability. To enhance the welding quality and production efficiency of safes, the ATINY laser seam tracking system has gradually become an important tool in automated welding. 　　Principle of Laser Seam Tracking System 　　The laser seam tracking system uses laser sensors to detect the position and shape of the weld seam in real-time. This data is then fed back to the welding control system, which adjusts the position of the welding torch and the welding parameters in real-time based on the feedback. This mechanism of real-time detection and adjustment effectively improves welding quality and reduces welding defects. 　　Challenges in Automated Welding of Safes 　　The welding process of safes faces the following challenges: 　　Complex and Variable Weld Seams: The structure of safes is complex, with diverse weld seam paths. Traditional welding methods struggle to ensure consistent welding quality. 　　High Precision Requirements: Safes demand high welding quality, as even minor welding defects can affect their safety performance. 　　Welding Deformation: Safes are often made of high-strength steel, which is prone to deformation and cracking during welding, imposing stringent requirements on the welding process. 　　Manual welding or blind welding with robots often fails to meet these requirements, leading to unstable welding quality and low production efficiency. 　　ATINY Laser Seam Tracking System Solution 　　In response to the challenges in automated welding of safes, the ATINY laser seam tracking system offers an effective solution. The system monitors the weld seam's position and shape in real-time using laser sensors, and adjusts the path with intelligent algorithms to ensure accuracy and stability in the welding process. The system features high precision, high speed, programmability, and stability, making it adaptable to different welding tasks and process requirements. 　　Advantage

　　With the continuous advancement of industrial technology, welding, as an essential joining process, has been widely applied in various fields. However, traditional welding methods suffer from low precision and inefficiency, failing to meet the demands of modern industrial production. The ATINY weld seam tracking system utilizes high-precision sensors, image processing technology, and advanced control algorithms to monitor and adjust the welding process in real time, thereby improving welding quality and production efficiency. 　　Components and Principles of the Weld Seam Tracking System 　　A weld seam tracking system typically consists of the following key components: 　　Sensors: Commonly used sensors include laser sensors and optical sensors. Laser sensors measure the shape and position of the weld seam by emitting laser beams and receiving the reflected light. Optical sensors capture images of the welding area through cameras for image processing. 　　Image Processing Module: The image processing module analyzes images of the welding area received from the sensors to identify the exact position and shape of the weld seam. Advanced image processing algorithms, such as edge detection, morphological processing, and deep learning, enable the system to precisely locate the weld seam. 　　Control System: Based on the weld seam information provided by the image processing module, the control system adjusts the movement path and parameters of the welding robot or welding head. Through real-time feedback control, it ensures precise positioning and stable movement of the welding torch along the weld seam. 　　Actuator: The welding robot or welding torch, serving as the actuator, performs welding operations according to the instructions from the control system. High-precision servo motors and motion controllers ensure the stability and accuracy of the welding process. 　　Applications of the Weld Seam Tracking System 　　The ATINY weld seam tracking system is widely used in the automotive manufacturing, shipbuilding, aerospace, and machinery manufacturing industries. These fields demand high welding quality and production efficiency, and the introduction of weld seam tracking

　　As an important part of modern manufacturing industry, automatic welding technology greatly improves production efficiency and welding quality. In the process of automatic welding, how to accurately locate the welding position is a very important problem. The accuracy of positioning directly affects the quality of welding and the consistency of welding seams. This article wants to create a small series of welding seam tracking system to take you to understand the common positioning methods and applications in automatic welding. 　　First, the importance of automatic welding positioning 　　Welding positioning refers to ensuring that the welding gun or welding robot can be accurately aligned with the part to be welded during the welding process. Accurate positioning can ensure the quality of welding and avoid welding defects caused by deviation, such as weld deviation and unstable welding. At the same time, good positioning can also improve production efficiency, reduce scrap rate and save costs. 　　Second, the commonly used automatic welding positioning method 　　1. Mechanical positioning 　　Mechanical positioning is achieved by clamping and fixing devices. The advantages of this method are simple structure, low cost, and suitable for mass production and regular shape of the workpiece. However, for production lines with complex shapes or requiring frequent workpiece changes, mechanical positioning is not flexible enough. 　　2. Visual sensor method 　　This method utilizes machine vision technology to identify and locate welding locations. By installing a vision sensor on the welding workpiece and using the corresponding software to process and analyze the image, the automatic recognition and positioning of the welding position can be realized. This method has the advantages of accurate positioning, fast speed and wide application range, but there are also some challenges, such as the influence of lighting conditions, sensor calibration and so on. 　　3. Laser sensor method 　　This method uses laser measurement technology to locate the welding position. By installing the laser sensor on the welding workpiece and using the corresponding software to process and analy

　　Water heaters are essential in daily life, and their market demand is continuously increasing. The inner tank, as the core component of a water heater, directly affects its performance and lifespan. Traditional welding processes for water heater inner tanks have various issues. To overcome these, ATINY has developed a laser seam tracking system that leverages advanced laser technology to achieve automation and intelligence in inner tank welding, significantly improving welding quality and production efficiency. 　　Principle of Laser Seam Tracking 　　The laser seam tracking system uses laser sensors to monitor the position and shape of the weld seam in real time and adjusts the welding path through intelligent algorithms. The main steps of its working principle are: 　　Laser Scanning: The laser sensor scans the welding area to obtain three-dimensional shape data of the weld seam. 　　Data Processing: The collected seam shape data is transmitted to the control system, where specific algorithms process and analyze the data to determine the actual position and shape of the weld seam. 　　Path Adjustment: Based on the analysis results, the control system adjusts the position and movement trajectory of the welding head in real time, ensuring the welding torch always follows the center of the seam. 　　Feedback Control: During the welding process, the laser sensor continuously monitors the seam position and welding quality, providing feedback to the control system for dynamic adjustments to ensure welding quality stability. 　　Challenges in Automated Welding of Water Heater Inner Tanks 　　Welding water heater inner tanks poses several challenges: 　　Complex Seam Shapes: Inner tanks usually have a cylindrical design with curved seams, making it difficult for traditional welding methods to ensure uniformity and consistency. 　　High Temperature and Pressure Environment: Water heaters need to withstand high temperatures and pressures, requiring weld seams to have excellent strength and sealing. Any slight mistake can result in weld leakage or cracks. 　　Material Characteristics: Inner tanks are mostly made of stainless steel, which is corrosion-resistant and strong bu

　　Welding is a great invention in industrial manufacturing and an indispensable technology in production. The welding process involves controlling heat or a heat source to act on two or more materials, forming a complete joint. For example, in arc welding, the welding operation involves a person, a robot, or a specialized holder moving the welding torch along the weld seam at a certain speed while applying heat energy according to specific process parameters. In addition to correct process parameters, the ability of the welding torch to accurately track the weld seam is crucial for ensuring welding quality. 　　Among various welding process information sensing methods, the visual method is recognized as providing the most information and the best results. As early as the early 1980s, many researchers at home and abroad began studying visual sensing methods, including passive visual sensing using arc light as the light source and active visual sensing with laser-assisted lighting. In passive visual methods, the arc itself monitors the position, avoiding advance detection errors caused by thermal deformation and directly obtaining information about the weld seam and molten pool, which is beneficial for adaptive control of welding quality. 　　However, direct observation is easily disturbed by the arc, and there are still no mature industrial applications reported. Therefore, active optical vision, especially structured light or scanning methods based on laser triangulation principles, has become the primary visual sensing method in welding industrial applications. The greatest feature of laser vision sensing is its ability to obtain precise geometric shapes and spatial positions of weld seam cross-sections, suitable for real-time weld seam tracking and adaptive process parameter control. 　　The basic principle of laser vision sensing is optical triangulation. A laser beam shines on the surface of the target object, forming a light spot. This spot produces an image point on a photosensitive detector through a lens on the camera. Since the relative position of the laser and camera is fixed, changes in the distance between the laser sensor and the target object cause correspondi

　　Welding is a critical process in manufacturing, widely used in automotive, aerospace, shipbuilding, and construction industries. With the advancement of technology, traditional welding quality inspection methods have gradually exposed inefficiencies and lack of precision, giving rise to visual monitoring technology in welding. Today, let's explore the application and challenges of visual monitoring technology in the welding process with the team from Tracking Creative Welding Seam Tracking System. 　　Application of Visual Monitoring Technology 　　Visual monitoring technology utilizes cameras and sensors to capture real-time images and data during the welding process. By employing image processing, pattern recognition, and other techniques, this technology enables real-time monitoring and quality inspection of the welding process. 　　Real-time Monitoring of Welding Parameters: By capturing images during the welding process, the visual monitoring system can analyze parameters such as welding voltage, current, and speed in real time, ensuring they meet preset requirements. For instance, if the welding current exceeds the normal range, the system can automatically issue an alert and adjust the parameters, thereby improving welding quality. 　　Automatic Detection of Welding Defects: Traditional welding defect detection often relies on human observation, which is prone to subjective influences and inefficiency. The visual monitoring system, however, can automatically detect welding irregularities, pores, cracks, and other defects through image processing and pattern recognition algorithms, issuing immediate alerts upon detecting defects. 　　Welding Quality Assessment: By analyzing image features and weld seam shapes during the welding process, the visual monitoring system can automatically calculate welding quality indicators, such as weld seam width, depth, and shape deviation. This helps determine whether the welding is qualified and provides suggestions for improving welding quality. 　　Data Recording and Analysis: The visual monitoring system can record data during the welding process and conduct in-depth data analysis. This helps identify potential issues in the

　　With the rapid development of industrial automation and intelligent manufacturing, welding, as a critical part of the manufacturing process, directly affects the performance and lifespan of products. Traditional weld seam inspection mainly relies on manual checks, which are not only inefficient but also prone to human error, leading to inconsistent inspection results. To address these issues, machine vision technology has been introduced into weld seam inspection, providing an efficient, accurate, and repeatable solution. 　　Basic Principles of Machine Vision 　　Machine vision inspection of weld seams primarily uses high-precision cameras and advanced image processing algorithms to achieve automatic, rapid, and accurate inspection. The main steps of machine vision inspection for weld seams are as follows: 　　Image Acquisition: Using high-resolution industrial cameras and precise optical systems, images of the workpiece after welding are captured to obtain high-definition images of the welding area. It is crucial to ensure the stability and clarity of the images during this process to guarantee the accuracy of subsequent processing. 　　Preprocessing: The captured images undergo noise reduction, contrast enhancement, and other operations to improve image quality, facilitating subsequent feature extraction and defect identification. 　　Feature Extraction: Image processing algorithms are used to extract features such as shape, size, and texture of the welding area. For example, edge detection algorithms can accurately identify the edges of weld points, which are critical for assessing the quality and position of the weld points. 　　Defect Identification: Based on the extracted feature information and predefined defect identification models, the system determines whether there are defects in the welding area, such as cracks, pores, slag inclusions, etc. 　　Generating Inspection Reports: The machine vision system can generate inspection reports, detailing the inspection status of each weld point, including quality grades, defect types, locations, and other information. This provides a basis for subsequent quality control and improvements. 　　Compared to traditional we