A complex site forced out of a brainwashed weld
According to ship brokerage firm Clarkson, in the first half of 2026, Chinese shipyards' new order collection totaled 31 million correctional units, an increase of 113%, representing 72% of the global market. At the same time, in the latest operational data announcement, the production of rolling steel structure products in the first half of the year increased by 14.30%, and the sales of refined steel structure steel products increased by 12.9% in the first half of the year.
For manufacturing companies, automation replacement is no longer a question of whether or not robots can get into jobs in the harsh environment, recruitment difficulties, and long-term dependence on skilled workers.
The difficulty is that these jobs are precisely the hardest part of traditional automation to reach.
Ship segments, non-specific steel structures and large engineering machinery components are commonly characterized by large size, large batches, significant thermal deformation, and installation errors and feed biases are commonplace. Some workpieces even lack three-dimensional models or engineering drawings that are fully compatible with physical objects, and the time spent on point-to-point demonstration, man-made programming and repeated modeling can easily offset the efficiency gains of automation.
Therefore, robots not only need to execute the preset trajectory accurately, but they also need to independently search for screws, understand the structure of the workpiece, plan the gun gesture, and modify the path and process parameters according to the field state. This is the fundamental reason why screws are pushed to the stage.
Representative companies in the high-tech robotics market have found that different companies build different brains: some build intelligent production lines based on drawings, models and engineering data; some emphasize full collaboration of vision, control, essence and processes; and some integrate hardware through eco-collaboration with models and software platforms at their core.
The market is large enough and the problems are difficult enough, so different companies build different paths to brainwash, but we can still gain insight into some of the commonalities between brainwashers.
One brain, a multi-shaped adaptation
In the past, welding robots, software, process packages, and robot bodies were often highly bound together. A set of systems typically served only fixed mechanical arms, workpieces, and tools. When mechanical arm models, installation methods, or operating objects changed, a large amount of demonstration, programming, and modeling work needed to be done again.
The emergence of brainwashing is changing this relationship. Whether enterprises call it brainwashing, brainwashing, brainwashing, or brainwashing, the technological direction behind it is basically the same: extracting task understanding, environmental perception, trajectory planning, and process decision-making from the very core of a specific robot to form an intelligent base that can fit different execution devices.
For example, the use of fixed equipment such as braces, suspensions, and locks to cover the braces of the welding system and to extend it further to mobile braided and quadruped welding robots, enabling the cognitive and autonomous decision-making capabilities of the welding process to be integrated into a more complex field with different entities.
Visual Bit's intelligent welding brain is capable not only of performing the entire process without demonstration on a dual-machine synchronous welding workstation, but also of providing flexible welding work for full-size welding robots.
Physical AI and humanoid robots, whose self-developed RoBIM CLOUD industrial software platform is compatible with various configurations such as industrial robots, collaboration robots, humanoid robots, and is evolving into an intelligent underpinning with the ability to manipulate cluster brain networks.
There is no denying that the technical logic of intelligent welding has changed: the brain is responsible for understanding tasks, identifying tools, generating trajectories and making process decisions, and then handing over movement and process instructions to different execution devices. Industrial robots, collaborative robots, mobile robots, quadrupeds and humanoids have all become different body parts that can access the same set of intelligent robots.
Fixed equipment focuses on rhythm, accuracy and stability; mobile, belted and crawling equipment mainly addresses the difficulty of robots in large components approaching rods; quadrupeds and humanoids further explore their adaptability in narrow spaces and unstructured environments.
Therefore, different forms of robots are likely to continue to coexist for a long time. Customers do not really need a unified robot shape, but rather a lower synthesis book, a higher productivity execution method for the structure of the workpiece, the positioning and the operating environment. For welding brains, the key question is no longer which kernel ultimately wins, but how many kernels the same set of welding brains can fit and which working conditions to enter.
A brain, behind the base of calculus
As with today's general model of AI, the scaling law of the welding brain is related not only to the quantity of the welding field data, but also to edge computation.
As the system simultaneously accesses more cameras, sensors, and robotic bodies, traditional controllers have become increasingly difficult to perform all sensing and reasoning tasks alone, and independent edge computing platforms have begun to enter the connecting units.
For example, from the very beginning of its founding, it has positioned itself as an industrial smart enterprise with models and software capabilities at its core. Unlike the traditional path of extending from grinding machines, mechanical arms or process packages to intelligentization, it has not tried to build all the hardware links on its own, but rather to integrate mechanical arms, vision and welding equipment in an eco-cooperative way.
In its released welding robot program, the mechanical arm uses superior products, the visual system uses a dedicated 3D welding camera from McCandless, and completes model reasoning through its self-developed model of the pet world, the Huongyang hardware platform and the positioning pen, and the software hardware connects to human interaction.
Small Rain Smart offers two SoC options on the Huongyang Hardware Platform, both the Orin chip with 200 TOPS computing power and the Penguin S600 chip with 560 TOPS computing power. Small Rain Smart believes in introducing a top-of-the-line computing platform that can enhance end-side model reasoning and motion generation capabilities, allowing robots to maintain high frequency local real-time decision making.
In addition to small-scale AI, there are also claims to increase the edge computing power of welding robots. Full-frame intelligent welding solutions that use layered design concepts, divide the intelligent welding system into the control layer, software layer, computing layer and cloud layer, and create two large edge computing systems for the computing layer WeldAI Edge IWS800/IWS900.
With a computing power greater than 50 TOPS, WeldAI Edge can extract 3D point cloud characteristics in 200ms, and achieve connection path planning and real-time response to correction biases.
Edge computing enters the welding unit, which does not mean simply stacking higher chip specifications. The welding scene is more focused on the closed-loop response, rather than simply pursuing model parameters. The perceptual results must be converted into trajectories and process instructions in a timely manner in order to quickly complete the correction after material deviations, thermal deformations and welding state changes occur.
The real value of edge computing platforms, therefore, is to keep the closed loop in place to implement edge-sensing edge-decision edges, reduce reliance on cloud connectivity, and ensure the certainty and continuity required for industrial production.
A brain that moves to a multifaceted process
What is happening at the same time as the diversification of robotics is that brainstorming companies are expanding into more manufacturing processes.
The world of robotics focuses on key processes in the field of metal processing, and has expanded its capabilities to areas such as cutting, grinding and assembly.
Visual bits have covered scenes such as sorting, grinding, assembly, welding, quality inspection and measurement;
Small-scale intelligent design plans are gradually expanding to industrial processes such as spraying, grinding, assembly, wiring;
Blue intelligence has begun to extend the boundaries of capability to cutting, grinding, spraying, etc.
The company said that it will continue to launch all kinds of industrial intelligent robot products such as grinding, cutting, assembly, and transportation.
Why are these companies so focused on more processes?
The reasons for this are not complicated, be it welding, cutting, grinding or spraying, although the process principles are different, the bottom line is to understand the geometry of the workpiece, identify the operating area, generate continuous trajectories, plan the robot gesture, and adjust the process parameters according to the materials, tools and on-site status.
The reason these enterprises are the first to deploy welding is because welding is one of the most difficult and data-value scenarios, which can run in the technical and commercial closures of welding, and have the opportunity to transfer some of the capabilities to adjacent processes.
However, the fact that aluminum can migrate aluminum does not mean that it can replicate aluminum. Between welded arc, cutting heat source, grinding contact and sprayed fluid, there are completely different physical mechanisms, end-to-end tools, safety logic and quality evaluation standards.
Although scenario understanding, task planning, robotic interfaces, and data infrastructure can be replicated, specific process models still need to be retrained, validated, and engineered. Only by combining general intelligence capabilities with long-term accumulated process know-how can brain enterprises truly cross the boundaries between different processes.
After the change of brains, the exam has just begun
It is necessary to acknowledge that the current welding brain is still a considerable distance from true maturity. Wang Deong, the founder and chairman of Weiwei Weiwei, judged that the capacity of welding large models at this stage is roughly equivalent to the level of ordinary welding workers with 4 to 5 years of work experience, and that in the future, as models evolve, it will reach the level of senior welding workers with 8 to 10 years of work experience.
In addition, some demonstration-free and autonomous programming systems have already been implemented on the rule components and some non-signal components, but in the face of superimposed factors such as strong reflection, complex slopes, thermal deformation, installation bias and multi-layered multi-way bridges, the stability of identification, planning and correction of biases still needs continuous verification.
Competition at the business level is also intensifying. Connecting brains companies are not only facing competition from similar startups, but also expanding their capabilities to deal with robotic embedded manufacturers, connecting equipment companies and system integrators. The latter are mastering the embedded, channels, processes and customer resources, and are accelerating the deployment of intelligent connecting software and algorithm platforms.
The whole logic of the traditional demonstration of the last thirty years has been changed from the fact that the clutch moves to the fact that the clutch moves to the fact that the position of the battlefield has been moved from its essence to the brain.
* Cover for AI mapping