When handling complex spatial curve welds, automatic soldering machines require multi-dimensional technology collaboration to achieve high-precision trajectory tracking control. The core of this lies in constructing a closed-loop system of "perception-decision-execution." First, the geometric characteristics of complex spatial curve welds mean that traditional teaching-reproduction methods are insufficient to meet accuracy requirements. For example, in scenarios such as ship sections and aerospace irregularly shaped components, welds may exhibit three-dimensional spirals, multi-axis intersections, or asymmetrical curved surfaces. Their spatial position, bevel angle, and radius of curvature continuously change, requiring the welding system to have real-time dynamic adjustment capabilities.
To achieve high-precision perception, modern automatic soldering machines generally employ multi-modal sensor fusion technology. Laser vision sensors scan the weld contour with line lasers to generate three-dimensional point cloud data, accurately identifying bevel type, gap width, and misalignment. Arc sensors monitor welding current and voltage fluctuations to infer the dynamic characteristics of the molten pool, making them particularly suitable for environments with strong arc interference. Infrared thermal imaging modules track the temperature field distribution in the weld area to prevent burn-through or incomplete fusion defects. After edge computing processing, the sensor data forms a real-time mapping of the weld seam's spatial coordinates, providing a foundation for trajectory planning.
The intelligent decision-making layer is the core control unit for trajectory tracking. Based on point cloud data from sensor feedback, the system reconstructs the 3D model of the weld seam using a B-spline curve interpolation algorithm, decomposing complex spatial curves into tiny line segments or arc segments to generate a continuous welding path. For areas with abrupt curvature changes, an adaptive oscillation welding algorithm dynamically adjusts the welding torch's oscillation frequency and amplitude to ensure uniform molten metal coverage. Simultaneously, a deep learning model, trained on historical welding data, can predict defects such as porosity and undercut, and correct welding parameters in advance. For example, in welding thin aluminum alloy plates, the system can automatically reduce heat input based on the material's thermal expansion coefficient to avoid deformation.
The execution control layer relies on high-precision motion mechanisms to achieve trajectory tracking. A six-axis industrial robot, driven by servo motors, can correct the welding torch's pose within millisecond response times, achieving a repeatability accuracy of ±0.05mm. For welding large structural components, the coordinated movement of the positioner and the robot is particularly important. Through a master-slave control algorithm, the positioner rotates or tilts the workpiece, ensuring the weld seam is always in the optimal welding position, while the robot handles the fine-tuning of the welding torch. For example, in welding the longitudinal seam of a storage tank, the positioner rotates the cylindrical shell to a horizontal position, and the robot completes straight-line welding along the axial direction. Their movements are synchronized via a time axis, avoiding interference.
Dynamic correction technology is crucial for handling welding process disturbances. Affected by factors such as heat input and workpiece assembly errors, the actual weld seam position may deviate from the preset path. The system calculates the deviation and generates correction commands by comparing sensor feedback data with theoretical models in real time. For example, when laser vision detects a lateral offset of the weld seam, the controller immediately adjusts the robot joint angle to return the welding torch to the correct position; if the arc sensor detects an abnormal current, it quickly corrects the welding speed or wire feed rate. This closed-loop control mechanism ensures the continuous stability of the welding trajectory.
Multi-process collaborative control further improves the welding quality of complex weld seams. For different material combinations, the system can automatically switch welding methods. For example, titanium alloy welding uses active flux argon arc welding (A-TIG) to enhance penetration, while dissimilar metal welding uses pulsed MIG welding to control heat input. Simultaneously, the wire feeding mechanism and shielding gas flow rate are dynamically adjusted according to the welding speed to ensure effective molten pool protection. For instance, in lap welding of stainless steel and carbon steel, the system adjusts the wire feeding speed and arc voltage to prevent excessive weld metal dilution.
Ultimately, the automatic soldering machine optimizes trajectory planning through a combination of offline programming and online teaching. Offline programming uses a 3D workpiece model to generate the initial path, reducing on-site debugging time; online teaching uses manual guidance of the welding torch to complete key point positioning, compensating for model errors. This combination allows the system to adapt to both standardized production and personalized customization needs. With the introduction of digital twin technology, the welding process can be simulated and verified in virtual space, further reducing trial-and-error costs and driving the development of complex spatial curve weld welding towards full automation and intelligence.
