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IPDBChina IndustryManufacturing & Processing MachineryIndustrial Robot



China, Chinese Industrial Welding Robot, Portable Welding Machine with Control Cabinet and Wire Feeder 6 Axis1 Industrial Products Supplier Manufacturer Details, price list catalog:

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Industrial RobotPortable Welding Machine with Control Cabinet and Wire Feeder 6 AxisCollaborative Welding RobotWelding Cobot

Taiyuan Jin Tai Technology Co,.Ltd

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Product Description  Collaborative Welding Robots: Driving Dual-Wheel Development of Skill Inheritance and Technological Innovation in Manufacturing WeldingIn the manufacturing industry, the welding sector faces a unique dual challenge: on one hand, a large number of senior welders with decades of experience are retiring, taking with them irreplaceable "craftsmanship skills" (like mastering complex weld angles for special materials or troubleshooting subtle process issues); on the other hand, the industry's shift toward advanced materials (titanium alloys, high-strength steel) and complex product designs demands continuous technological innovation in welding processes. Traditional solutions fall short: manual welding relies on oral instruction and on-the-job training, making skill inheritance slow and inconsistent; traditional robotic welding automation replaces human expertise with rigid programming, losing the "human touch" of skilled welders and struggling to adapt to innovative processes. It's in this context that the collaborative welding robot (or welding cobot) has emerged as a bridge between the past and future, redefining human-robot collaboration welding to enable both skill inheritance and technological innovation-driving manufacturing welding toward a new era of "craftsmanship + intelligence".The collaborative welding robot's ability to "record and replicate" skilled welders' techniques is the key to solving the skill inheritance crisis. Unlike traditional robots that require programmers to translate welding skills into code (a process that often loses critical nuances), welding cobots can directly "learn" from senior welders via hand-guided teaching. A senior welder can hold the robot's arm, guiding it through a complex weld (like a multi-pass weld on a thick steel pipe) while adjusting pressure, speed, and torch angle-just as they would when teaching an apprentice. The robot records every parameter of this process (down to 0.01mm adjustments in position) and stores it as a digital "skill template". New operators can then call up this template, and the robot will replicate the senior welder's technique with perfect consistency-eliminating the "skill gap" between generations. In human-robot collaboration welding setups, this inheritance becomes even more dynamic: senior welders can refine the robot's "skill template" by making real-time adjustments during operation, while new operators learn by observing the robot's movements and the welder's guidance. For example, a heavy machinery manufacturer in Germany has a 65-year-old senior welder who specializes in welding large excavator booms. By teaching his techniques to three collaborative welding robots, the company has preserved his "craftsmanship"-even as he transitions to part-time work-and reduced the training time for new operators from 2 years to 6 months. This not only avoids skill loss but also ensures that every boom weld meets the same high standard as the senior welder's work.The Cobot Welding System amplifies this skill inheritance by turning "tacit knowledge" into "digital assets" and making it accessible across the enterprise. A modern Cobot Welding System includes a cloud-based "skill library" that stores all the digital "skill templates" created by senior welders. Operators in different workshops or even different factories can access these templates via the system's interface, ensuring that the same high-level welding skills are applied consistently across the entire organization. Unlike traditional welding robotics system setups, where skills are tied to individual programmers or local machines, the Cobot Welding System's skill library turns personal expertise into shared organizational knowledge. For a multinational manufacturer of marine equipment, this means that a senior welder's technique for welding corrosion-resistant stainless steel (developed in their Japanese factory) can be quickly deployed to their U.S. and Brazilian factories via the cloud-ensuring that all marine propeller shafts meet the same quality standard, regardless of location. The system also includes a "skill optimization" feature: when multiple senior welders contribute templates for the same process, the system uses welding automation technology to analyze and combine the best elements of each, creating an even more refined "master template". This collaborative skill refinement is impossible with manual training, where individual welders often stick to their own techniques.While preserving traditional skills, collaborative welding robots also drive technological innovation by enabling the testing and implementation of new welding processes that were once too risky or complex for manual or traditional robotic systems. Advanced materials like titanium alloys (used in aerospace) or carbon fiber composites (used in new energy vehicles) require extremely precise control of welding parameters-even a 1% deviation in current can cause material damage. Welding cobots, equipped with real-time sensor feedback and AI-powered parameter adjustment, can handle these materials with ease. For example, when welding titanium aircraft components, the robot's sensors monitor the temperature of the weld pool 100 times per second, and its AI system adjusts the welding speed to keep the temperature within the optimal range (between 800-900°C)-a level of precision no human welder can match. The Industrial welding cobot, built for high-intensity, long-duration operations, takes this innovation further: it can test new welding processes (like laser-arc hybrid welding) for hours on end, collecting data on weld strength, porosity, and material deformation. Manufacturers can then analyze this data to refine the process before scaling it to production. A leading aerospace manufacturer used Industrial welding cobots to develop a new process for welding thin-walled titanium fuel tanks-reducing weld time by 50% and increasing fuel tank durability by 30% compared to the traditional manual process. This kind of innovation not only improves product performance but also opens up new possibilities for designing lighter, stronger components.The Collaborative Welding Cell serves as a "innovation lab" for welding processes, integrating the welding robotics system with testing tools and digital simulation to accelerate innovation. A Collaborative Welding Cell includes 3D scanners that measure weld quality in real time (detecting defects like micro-cracks that are invisible to the naked eye), and digital twin technology that lets manufacturers simulate new welding processes in a virtual environment. Before testing a new process on physical parts, engineers can use the digital twin to model how the collaborative welding robot will perform-adjusting parameters like torch angle, wire feed rate, and shielding gas mix to optimize results. This eliminates the risk of wasting expensive materials (like titanium) on failed trials. For example, a manufacturer of hydrogen fuel cells used the Collaborative Welding Cell's digital twin to simulate welding thin stainless steel bipolar plates (a critical component of fuel cells). By testing 20 different process configurations in the virtual environment, the team found the optimal parameters that minimized weld deformation-then implemented the process on the physical robot with a 95% success rate on the first try. Without the digital twin, this innovation would have taken 6 months and wasted $50,000 in materials; with the Collaborative Welding Cell, it was completed in 3 weeks at a fraction of the cost.Advances in welding automation technology are the foundation of both skill inheritance and technological innovation. Machine learning algorithms allow welding cobots to "improve" the skills they inherit-by analyzing hundreds of welds using a senior welder's template, the robot can identify small adjustments (like slightly increasing speed for thicker material) that even the welder might not have noticed, enhancing the template over time. AI-powered defect prediction helps in innovation: the system can analyze data from new welding processes to predict potential issues (like porosity in aluminum welds) and suggest parameter changes to prevent them. Additionally, the integration of augmented reality (AR) into Cobot Welding System interfaces allows senior welders to overlay their "virtual guidance" onto the robot's work-new operators can wear AR glasses to see the senior welder's hand movements superimposed on the robot's arm, making skill learning more intuitive. These technological advancements ensure that collaborative welding robots don't just preserve the past-they also build the future of welding.As a core component of automated welding solutions, collaborative welding robots are redefining the role of humans in welding: senior welders become "skill architects," designing and refining digital templates; new operators become "robot collaborators," monitoring and optimizing the robot's work; and engineers become "process innovators," using the robot to test and develop new techniques. This shift not only solves the skill shortage crisis but also creates a more innovative, sustainable welding industry-one where traditional craftsmanship and cutting-edge technology work hand in hand.In conclusion, collaborative welding robots are more than a tool-they're a catalyst for the dual-wheel development of skill inheritance and technological innovation in manufacturing welding. Through human-robot collaboration welding, they preserve the "craftsmanship" of senior welders; via the Cobot Welding System, they turn tacit knowledge into shared digital assets; with the Industrial welding cobot, they enable large-scale testing of innovative processes; in the Collaborative Welding Cell, they accelerate innovation with digital simulation; and leveraging welding automation technology, they continuously refine both skills and processes. For manufacturers looking to honor their heritage while embracing the future, collaborative welding robots are the key to balancing tradition and innovation. The future of welding isn't just automated or manual-it's a harmonious blend of both, driven by collaborative welding robots.The manufacture of this series of welding machines complies with the standard GB15579.1-2004 "Arc welding equipment part 1: welding power supply". The MIG-P series inverter pulse MIG/MAG arc welding machine has two welding modes: P-MIG and conventional MIG.The P-MIG welding mode can achieve carbon steel and stainless steel.For the welding of non-ferrous metals, the MIG welding mode can achieve low spatter welding of carbon steel and CO2 gas shielded welding.The performance characteristics are as follows:Fully digital control system to achieve precise control of the welding process and stable arc length.Fully digital wire feeding control system, accurate and stable wire feeding.The system has a built-in welding expert database and automatic intelligent parameter combination.Friendly operation interface, unified adjustment method, easy to master.Minimal welding spatter and beautiful weld formation.100 sets of welding programs can be stored to save operation time.The special four-step function is suitable for welding metals with good thermal conductivity, and the welding quality is perfect when starting and ending the arc.It has various interfaces for connecting with welding robots and welding machines (optional). PWM inverter technology can improve the reliability of the whole machine, high precision, energy saving and power saving.Precautions for use(1) The equipment number plate should be riveted at the specified position on the upper cover of the casing, otherwise the internal components will be damaged.(2) The connection between the welding cable and the welding machine output socket must be tight and reliable. Otherwise, the socket will burn out and cause instability during welding.(3) Avoid contact between the welding cable and metal objects on the ground to prevent short circuit of the welding machine output.(4) Avoid damage and disconnection of the welding cable and control cable.(5) Avoid deformation of the welding machine by impact and do not pile heavy objects on the welding machine.(6) Ensure smooth ventilation.(7) When used outdoors, the welding machine should be covered in rainy and snowy weather, but ventilation should not be hindered.(8) The maximum cooling water temperature should not exceed 30oC, and the minimum should not be frozen. The cooling water must be clean and free of impurities, otherwise it will block the cooling water circuit and burn the welding gun.2. Regular inspection and maintenance of the welding machine(1) Professional maintenance personnel should use compressed air to remove dust from the welding power supply once every 3 to 6 months, and pay attention to check whether there are loose fasteners in the machine.(2) Check the cable for damage, the adjustment knob for looseness, and the components on the panel for damage.(3) The conductive nozzle and wire feed wheel should be replaced in time, and the wire feed hose should be cleaned frequently.3. Welding machine faults and troubleshootingBefore repairing the welding machine, the following checks should be performed:(1) Whether the status and welding specification display on the front panel of the welding machine are correct, and whether the buttons and knobs are working properly.(2) Whether the line voltage of the three-phase power supply is within the range of 340V~420V; whether there is a phase loss.(3) Whether the connection of the welding machine power input cable is correct and reliable.(4) Whether the grounding wire connection of the welding machine is correct and reliable.(5) Whether the welding cable connection is correct and the contact is good.(6) Whether the gas circuit is good, and whether the gas regulator or proportioner is normal.

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