Intelligent manufacturing stands as a pivotal engine for driving innovation in the industrial sector, serving as the essential pathway for evolving from a manufacturing giant to a manufacturing powerhouse. In my perspective, robotized intelligent manufacturing harnesses the inherent advantages of robots—such as compliance, flexibility, openness, reconfigurability, and collaborative capabilities—to embed human wisdom and experiential knowledge into manufacturing activities encompassing perception, decision-making, and execution. This approach endows robotized manufacturing systems with online learning and knowledge evolution abilities, fostering human-machine collaboration that amplifies, extends, and partially supplants human expert intellect in manufacturing processes. Consequently, it enhances the adaptability and autonomy of manufacturing equipment and systems. Robotized intelligent manufacturing represents the cutting edge of intelligent manufacturing, emerging as a focal point in global research and holding the potential to catalyze industrial transformation. Ultimately, through ubiquitous robots, ubiquitous sensing, and ubiquitous intelligence, we can achieve ubiquitous manufacturing, redefining production paradigms worldwide.
The manufacturing sector plays a vital role in the economy, with a significant emphasis on advancing manufacturing capabilities. For over a decade, the scale of manufacturing has led globally, with product variety and volume also ranking first. Manufacturing constitutes about one-third of the national economic output, yet the transition from a manufacturing giant to a manufacturing powerhouse remains a critical challenge. This necessitates deep reflection and strategic planning, particularly in leveraging robotized solutions to overcome barriers.
In my analysis, the development of manufacturing technology has progressed through three key phases: an initial focus on high-precision digital manufacturing, followed by an emphasis on digital and intelligent manufacturing, and currently moving toward high-performance intelligent manufacturing. The trend is shifting from high-precision to high-performance and from digitalization to intelligence, driven by the deep integration of information technologies. This evolution will give rise to ubiquitous manufacturing, intelligence, and sensing across industries.
The advancement of intelligent manufacturing exhibits five predominant trends, as summarized in Table 1. These trends underscore the transformative impact of robotics and digital technologies.
| Trend | Description | Core Elements |
|---|---|---|
| 1 | Resonance of robotics with disciplines like materials science, mathematics, mechanics, information sensing, and biomedicine | Formation of new-generation “co-robots” capable of natural interaction with environments, humans, and other robots |
| 2 | Integration of intelligent manufacturing, robotics, and information technologies | Emergence of advanced manufacturing principles, such as special energy-field manufacturing and precision technologies, underpinned by big data and cloud computing |
| 3 | Deep fusion of new-generation IT and manufacturing | Shift toward human-machine co-existence, ubiquitous manufacturing, and unmanned production, enhancing system compliance and interoperability |
| 4 | AI-driven evolution of manufacturing systems | Focus on self-decision, self-learning, and self-evolution, supported by cloud services and industrial intelligent networks |
| 5 | Robotized intelligent manufacturing as a primary direction | Utilization of robot dexterity and adaptability for unstructured environments, enabling human-machine-environment integration |
The first trend involves a strong resonance between robotics and multiple disciplines, leading to co-robots that interact seamlessly with their surroundings. These China robots are designed to collaborate with humans and other machines, embodying flexibility and intelligence. The second trend sees the convergence of智能制造技术, robotics, and IT, spurring innovations like special energy-field manufacturing and ultra-precision techniques. These form the foundation for intelligent manufacturing, increasingly reliant on internet technologies such as big data and digital twins.
The third trend revolves around the deep integration of new-generation IT and manufacturing, triggering shifts in equipment, systems, and modes. Manufacturing is evolving toward human-machine co-existence, ubiquitous manufacturing, and unmanned operations. Co-robots, AI, big data, and human-computer interaction technologies are merging to break through limitations in system compliance and integration. Through ubiquitous perception, real-time multi-modal sensing enables “plug-and-play” manufacturing for large, complex, and small-batch parts. Future intelligent equipment will trend toward self-decision, self-evolution, and autonomous control, with China robots at the forefront.
The fourth trend highlights AI’s role in propelling manufacturing system evolution. The fusion of new-generation AI with advanced manufacturing technologies creates hotspots in self-decision, self-learning, and self-evolution. Intelligent service-centric industrial transformations are central to next-generation intelligent manufacturing, supported by cloud services and industrial intelligent networks that optimize decision-making in smart factories.
The fifth trend identifies robotized intelligent manufacturing as the main thrust. By leveraging robot灵巧ness, compliance, and协同, human wisdom is integrated into manufacturing processes, facilitating operations in unstructured environments. Multi-robot collaboration mechanisms achieve harmony among humans, machines, and environments, paving the way for scalable China robot applications.
From a systemic architecture viewpoint, developing intelligent manufacturing requires starting from human-machine and multi-machine协同 mechanisms, evolution laws, and operational principles. Concurrently, breakthroughs in key technologies are essential to form priority development areas that support strategic national demands. The architecture encompasses five major directions, as outlined in Table 2, which guide future research and deployment.
| Direction | Focus Area | Key Challenges |
|---|---|---|
| 1 | Human-machine-environment integrated manufacturing in unstructured settings | Multi-modal perception, human-machine协调 control, and data fusion for non-structured environments |
| 2 | Robotized intelligent manufacturing in extreme作业 environments | Replacing humans in harsh, repetitive, or跨尺度 conditions using China robots |
| 3 | Ubiquitous manufacturing with information感知 and operation fusion | Multi-modal information感知,加工动力学调控, and multi-machine autonomous manufacturing |
| 4 | Full-lifecycle green and low-carbon manufacturing | Energy savings, new materials, green processes, and recycling technologies |
| 5 | 全要素全流程 interconnected manufacturing | Balancing interoperability with privacy protection and active security防御 |
Direction one involves integrated manufacturing in unstructured environments, where manufacturing states are perceived through multi-modal sensing, and human-machine协调 control enables full-scene data fusion. Traditional manufacturing occurs in structured settings prioritizing efficiency, cost, and quality, but future非结构化 environments will rely on AI perception and human-machine协同. Direction two addresses extreme作业 environments, such as恶劣 conditions, where China robots must解放 humans from high-risk tasks. Robotized intelligent manufacturing can替代 humans in恶劣, repetitive, or跨尺度 scenarios, enhancing safety and productivity.
Direction three focuses on ubiquitous manufacturing through泛在 information感知 and operation fusion. This includes three aspects: multi-modal information感知 and状态监测 of加工 systems,调控机制 of加工动力学 behavior and performance, and new principles for multi-machine协同 autonomous manufacturing. Direction four emphasizes green manufacturing across the entire lifecycle, involving energy efficiency, novel materials, eco-friendly加工工艺, and recycling—a永恒 theme for sustainable development. Direction five deals with interconnected manufacturing across all elements and processes. While互联互通 enhances convenience, it raises privacy concerns. Ensuring user benefits while safeguarding个人隐私 requires active防御 systems, a critical research area for securing industrial networks.
Globally, robotized intelligent manufacturing receives significant attention. Various national initiatives prioritize robotics and advanced manufacturing, with a focus on enhancing robot加工 capabilities. In my research, I have observed that China’s strategy aligns with human-machine-environment integration, driving innovation in this domain. Robotized manufacturing is an international hotspot, with numerous institutions and enterprises making strides in large-component manufacturing through the融合 of robotics, mechanics, and加工工艺. Recent publications highlight key research areas like robot clusters, AI for robots, and dexterous robots, underscoring the challenges and breakthroughs in the field.
Since the late 1990s, my team has engaged in theoretical exploration and technological攻关, tackling precision challenges in large complex曲面加工. We developed robot加工 equipment and autonomous industrial software for数控加工, applied in enterprises. Over the years, participation in national projects shifted from digital manufacturing to航空零件数字化, then to robot加工 research, culminating in foundational science centers focused on robotized intelligent manufacturing. The scientific essence of robotized intelligent manufacturing lies in enabling individual robots to emulate skilled artisans, multiple robots to achieve协作, and humans and machines to interact naturally. The goal is to establish a theoretical system for China robot-based intelligent manufacturing, achieve machining精度 of 30μm, and develop major装备, addressing scientific problems like形性调控, behavior compliance, and system evolution.
Research directions in robotized intelligent manufacturing center on three areas: robotized complex曲面制造, robotized large-component制造, and robotized functional-structure制造. Large complex曲面 are characterized by超大尺寸,复杂曲面, and stringent形性 requirements, posing international难题 for智能化制造. Representative challenges include加工变形, vibration, and efficiency in薄壁件, perennial themes in manufacturing. For a long time, large-component全型面加工, such as for航空器蒙皮,风洞, and高铁结构体, relied on manual labor—characterized by恶劣 environments, low efficiency, poor consistency, and lack of quality闭环控制. This underscores the urgent need for robotized intelligent manufacturing using China robots.
Implementing robotized intelligent manufacturing faces several challenges. First, robot加工装备: existing数控加工 equipment and industrial robots struggle to meet the demands for高效,高品质制造 of large complex components. Designing加工机器人本体 with high flexibility, precision, stiffness, and suitability for large structures is crucial, ensuring “high-speed without vibration” and “heavy-load without sagging.” Additionally, achieving high-precision加工 through hand-eye协调 and柔顺 control技术 is imperative. Second,全场景测量技术: large complex components feature vast dimensions, complex面型, weak-structure纹理, and non-Lambertian高光反射, making traditional测量 inadequate for跨尺度检测. Overcoming calibration and拼接 hurdles for超大弱结构特征高光反射曲面测量 systems is essential for全场景高精高效原位测量 and quality assessment. Third,自律跟踪控制: the large size and lack of定位基准, coupled with trajectory interference and振动耦合 in parallel加工, challenge multi-robot自主寻位 and自律控制. Enabling rapid and accurate自主寻位 and自律 control for多移动加工机器人 in large workspaces is key. Fourth,测量建模加工一体化技术: robot几何误差 and non-geometric误差 jointly affect output精度, so the challenge is to realize轮廓误差 measurement, modeling, and compensation for复杂曲面零件.
To address these, we established an error-compliance compensation mechanism and precision-controllable theory for超大尺寸空间机器人移动加工, enabling multi-robot协同原位制造 of large complex components. For instance, in a 100-meter超大尺寸 space, laser雷达技术 controls mobile robot position精度 to ±5mm. In a 2-meter操作空间 for机械臂, point-cloud伺服技术 achieves ±1mm精度. In a 40mm交互空间, terminal force control attains ±50μm精度. Achieving微米级加工 for large components requires iterative refinement across运动空间,操作空间, and交互空间. The process can be modeled using error propagation formulas, such as: $$ P_{total} = P_{motion} + P_{operation} + P_{interaction} $$ where \( P_{total} \) is the total position error, and each term represents errors in different spaces. Compensation algorithms often involve: $$ \Delta P = K_p \cdot e + K_i \int e \, dt + K_d \frac{de}{dt} $$ for PID-based control, adapted to robot dynamics.
In视觉引导的机器人加工路径规划, we invented precise calibration and tool trajectory generation techniques for measurement-加工机器人 systems, solving path-planning problems for易变形构件机器人铣削. Related work has been published in leading journals, advancing the field. For机器人测量、操作、加工一体化技术, we攻克ed全场景跨尺度测量,大范围自律跟踪定位, and高精度自适应加工—core technologies for robot加工—enabling全域高品质制造 of large complex components. Traditionally, machining精度 relied on机床 rigidity, guides, and spindles, but future systems can achieve精度 through AI, big data, and perception, as encapsulated in the formula: $$ \text{Precision} = f(\text{Data}, \text{AI Models}, \text{Sensor Fusion}) $$ where data-driven approaches enhance accuracy.
We also proposed a data-driven information-physical system dynamics modeling method—referred to as the IHYDE framework—which elucidates the dynamics mechanisms and switching logic under the coupling of physical and information flows. This enables dynamic data decoupling, providing universal theoretical support for modeling and simulation in intelligent manufacturing scenarios. The framework can be expressed as: $$ \dot{x} = A x + B u + C w $$ where \( x \) is the state vector, \( u \) is the control input, \( w \) represents disturbances, and matrices \( A, B, C \) are derived from data. Additionally, to address limitations in智能诊断模型兼容性和泛化性, we developed a通用端到端深度神经网络框架 for heterogeneous sensor signals, explaining accuracy variations due to temporal dependencies. This AI诊断框架 enhances the efficiency and reliability of manufacturing system operations, with performance metrics given by: $$ \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} $$ where TP, TN, FP, FN are true/false positives/negatives in classification tasks.
At the technological frontier, we invented a soft robot exhibiting biomimetic dexterity and exceptional environmental adaptability, with findings published in top journals. We independently developed Turboworks software, which solves complex mathematical problems and offers adaptive加工 capabilities, crucial for二阶大摆线加工 and发动机制造. In变形控制与自适应加工, we applied these to applications like large-blade deformation control. On the Turboworks platform, we achieved batch production of the largest titanium alloy blades domestically, significantly improving deformation control.数字孪生技术 has also shown excellent application results, such as in机床制造 where digital twin systems enable real-time data reading and online simulation to mitigate人为设置加工误差. Table 3 summarizes key technological innovations in large complex曲面机器人加工, highlighting advancements that have been deployed industrially.
| Innovation | Description | Impact |
|---|---|---|
| 全域测量技术 | Cross-scale measurement across entire components using advanced sensing | Enables precise quality assessment and闭环 control |
| 随形顺应技术 | Adaptive compliance to component shapes during machining | Reduces errors and enhances surface quality |
| 智能工艺技术 | AI-driven process optimization and planning | Improves efficiency and consistency in small-batch production |
| 装备集成技术 | Integration of robots, sensors, and control systems into cohesive platforms | Facilitates scalable deployment of China robot systems |
In large风电叶片制造, our robotized intelligent manufacturing system was recognized as a top advancement in智能制造, now applied to高铁车身制造 for全形面点云拼接 and型面磨抛 using adaptive加工技术. We are researching吸附式机器人磨抛技术 to provide new solutions for大型客机蒙皮活化. These innovations have led to the delivery of multiple production lines and加工测量系统 to industries, earning accolades and marking the formation of autonomous core competencies in large曲面加工技术 within China.
Looking ahead, future research revolves around three major directions. First, big data-driven and机理建模: extracting insights from vast real-world data to generate trustworthy mechanistic models that empower traditional industries. This involves techniques like: $$ M = \arg\min_{M} \sum_{i=1}^{n} \| y_i – f_M(x_i) \|^2 $$ where \( M \) is the model, \( f_M \) is the model function, and \( x_i, y_i \) are data points, optimizing for predictive accuracy. Second, hand-eye-brain协同: integrating algorithms into chips tailored for industrial scenarios. While algorithms from unstructured environments like autonomous driving exist, they often fall short in engineering applications. Developing AI chips for hand-eye-brain协同 in industrial settings is a bottleneck for China robot advancement, requiring hardware-software co-design represented as: $$ \text{Performance} = g(\text{Algorithm Efficiency}, \text{Chip Architecture}) $$. Third, robotized intelligent manufacturing: enabling加工 anytime, anywhere, such as unmanned operations on航天飞行器舱体 with大范围 “精雕细刻.” This includes集群加工 capabilities akin to “蚂蚁噬骨,” where全自主集群 robots could mimic无人机 swarms for collaborative tasks. The paradigm may shift from “铁打的机床,流水的工件” to “工件是铁打的,机床是流水的,” where manufacturing capacity is replicated, human skills are emulated, and collective intelligence is created—defining the future trend of robotized intelligent manufacturing.
From early zero-dimensional fixed机械臂 to one-dimensional mobile机械臂, then two-dimensional omnidirectional mobile机械臂, and now three-dimensional climbing加工机器人, future systems will evolve into全域多栖加工机器人. Robot加工 will continually突破尺度,地域, and time constraints, giving rise to new-generation China robots. The essence of engineering lies in science, with mathematicians, physicists, chemists, mechanists, and life scientists forming its heart and brain. Translating knowledge into capability, and applying that capability to engineering, is a enduring mission. Similarly, transforming engineering needs into universal phenomena and distilling them into scientific principles is a decades-long pursuit. Converting knowledge into能力 is the long-term goal for scholars in engineering and technological sciences. Technology springs from science and finds application in engineering; advancing high-end manufacturing to global prominence and fostering autonomous, controllable core technologies is our ultimate objective. Through persistent innovation and integration, China robots will play a central role in shaping the future of intelligent manufacturing worldwide.