机器人GMA-AM过程多源信息特征融合建模及其智能控制研究

Robotic arc additive manufacturing (AAM) has a wide application prospect in the field of metal 3D printing due to its many advantages, such as high forming efficiency, low cost, good mechanical properties, short manufacturing cycle, more open system and no limitation of parts' structure size and materials. However, due to the complexity and variability of forming process, it is difficult to obtain effective forming characteristic information and the feedback control methods. The robustness of forming process is poor, and the scope of AAM application is very limited. In order to solve this bottleneck problem, this project takes the forming process control of robotic GMA-AM as the research object, and uses some sensors to characterize the dynamic process, such as vision sensor, temperature sensor, arc sensor and spectral sensor. And some technology is studied to realize the intelligent control of robotic GMA-AM, for example, the algorithm of DF-MDF based on data information drive, the modeling method of VPRS and dual-mode composite controller with the adaptive switch based on fuzzy controller. This works will provide a systematic theoretical basis and technical support for the realization of high-level and intelligent robotic arc additive manufacturing, which can meet the urgent technical requirements of high-end manufacturing fields, such as nuclear power, aviation, aerospace and weapons equipment.

机器人电弧增材制造因其成形效率高、成本低、力学性能好、制造周期短、系统开放不受零件结构尺寸和材料限制等优点，在金属3D打印领域具有广阔的应用前景。但因其成形过程复杂多变难于获得有效的成形特征信息，以及影响因素众多缺乏有效的反馈控制方法，导致其成形过程鲁棒性差，限制了其应用的推广。为解决这一瓶颈问题，本课题以机器人熔化极气体保护电弧增材制造（GMA-AM）成形过程控制为研究对象，利用视觉、温度、电弧和光谱等多传感信息来表征其动态成形过程，引入基于数据驱动的数据场多源信息融合算法(DF-MDF)和变精度粗糙集建模方法(VPRS)，设计基于传感信息的自适应模糊切换的双模复合控制器对机器人GMA-AM成形过程进行智能控制。为实现高水平、智能化的机器人电弧增材制造提供系统的理论基础和技术支撑，满足核电、航空、航天和武器装备等高端制造领域对电弧增材制造技术应用的迫切需求。

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