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首页> 外文期刊>Journal of Materials Processing Technology >Artificial intelligence identification of process parameters and adaptive control system for deep-drawing process
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Artificial intelligence identification of process parameters and adaptive control system for deep-drawing process

机译:深冲过程的过程参数人工智能识别与自适应控制系统

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摘要

A new in-process identification method of material properties and lubrication condition in the deep-drawing process of anisotropic sheet metals is proposed and applied to the adaptive process control ofthe blank holding force (BHF). The method is based on a combination model of artificial neuralnetwork (ANN) and elastoplastic theory. Three delegated plastic deformation properties, i.e. n value,F value and plastic anisotropic coefficient r, were identified using the measured process information atthe beginning of the process by means of ANN. The friction coefficientμ and the optimal BHFcontrol path were then calculated from the theoretical model. Furthermore, the friction coefficientwas monitored during the entire process, and a closed-loop control was applied to modify the BHFpath corresponding to the frictional variation. Experimental results show that the artificial intelligence(AI) control system can cover a wide range of both materials and influential parameters, such asfriction and ambient temperature automatically. It is confirmed that the newly developed system is avalid alternative for the quick responsible control system with high flexibility.
机译:提出了一种新的各向异性板材深冲过程中材料性能和润滑条件在线识别方法,并将其应用于毛坯夹持力(BHF)的自适应过程控制。该方法基于人工神经网络(ANN)和弹塑性理论的组合模型。在过程开始时通过ANN使用所测得的过程信息确定了三个代表的塑性变形特性,即n值,F值和塑性各向异性系数r。然后从理论模型中计算出摩擦系数μ和最佳BHF控制路径。此外,在整个过程中都监测了摩擦系数,并应用了闭环控制来修改与摩擦变化相对应的BHF路径。实验结果表明,人工智能(AI)控制系统可以自动覆盖各种材料和影响参数,例如摩擦和环境温度。可以肯定的是,新开发的系统是具有高度灵活性的快速负责的控制系统的有效替代方案。

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