LMD工艺过程的无模型自适应迭代学习控制  

Model-Free Adaptive Iterative Learning Control for LMD Processes

作　　者：张煜杭 殷鸣[1] 李伟[1] 向锦 丁鑫钰 Zhang Yuhang;Yin Ming;Li Wei;Xiang Jin;Ding Xinyu(School of Mechanical Engineering,Sichuan University,Chengdu 610065,Sichuan,China)

机构地区：[1]四川大学机械工程学院,四川成都610065

出　　处：《应用激光》2024年第12期12-21,共10页Applied Laser

基　　金：国家自然科学基金(52075352);四川省科技计划项目(2021YJ0055、2020ZDZX0014)。

摘　　要：激光熔化沉积(LMD)可实现复杂构件的高性能制造,但是其工艺过程受多因素影响,构件成形过程的稳定性和质量一致性往往难以保证。现有的控制方案包括基于物理模型和数据驱动的方法。考虑到难以建立准确的机理模型来描述复杂的LMD工艺过程,因此采用数据驱动的方法对控制器进行设计。针对多层LMD工艺具有逐层堆积的特性,本研究提出一种基于无模型自适应迭代学习控制(MFAILC)算法的控制器。MFAILC作为一类智能控制策略,能够控制具有重复运行特性的复杂和时变系统,并在多种领域得到了应用。在自建的LMD系统进行了多组实验,利用与系统集成的热成像仪对熔池温度进行采集和提取。考虑到对控制器参数的整定,构建基于实验数据集的神经网络模型,对工艺过程进行模拟。通过典型环形薄壁结构件的沉积实验对设计的MFAILC控制器进行验证。与恒定参数下的实验相比,使用MFAILC控制器打印的环形薄壁结构件的沉积高度和宽度的极差分别降低了28.6%和15.4%,平均绝对误差分别降低了31.5%和48.8%,均方根误差分别降低了33.4%和35.0%。实验结果与对比分析表明,所设计的MFAILC控制器能够有效提高工艺过程的稳定性和制造部件的质量属性。Laser melting deposition(LMD)has the potential to achieve high-performance manufacturing of complex components,however,its process is affected by multiple factors,and ensuring the stability and consistency of the component formation process can be challenging.Existing control schemes include physical model-based and data-driven approaches.Considering the difficulty in establishing accurate mechanistic models to describe the complex LMD process,data-driven methods are employed for controller design.For the layer-by-layer processing principle of multi-layer LMD process,this study proposes a controller based on the Model-Free Adaptive Iterative Learning Control(MFAILC)algorithm.As an intelligent control strategy,MFAILC has the ability to control complex and time-varying systems with repetitive running characteristics and has been applied in various fields.Multiple experiments were conducted on a home-built LMD system,and the thermal imaging camera integrated with the system was used to collect and extract the temperature of the melt pool.To tune the controller parameters,a neural network model based on the experimental dataset was constructed to simulate the process.The designed MFAILC controller is validated by printing experiments based on typical annular thin-walled specimens.Compared with the experiments under constant parameters,the range of deposition height and width of the annular thin-walled structure printed using the MFAILC controller was reduced by 28.6% and 15.4%,respectively,and the MAE was reduced by 31.5% and 48.8%,respectively,and the RMSE was reduced by 33.4% and 35.0%,respectively.The experimental results and comparative analysis show that the designed MFAILC controller can effectively improve the stability of the process and the quality attributes of manufactured parts.

关 键 词：激光熔化沉积 熔池温度 神经网络建模 无模型自适应迭代学习 

分 类 号：TG485[金属学及工艺—焊接]