A neural network thrust force controller to minimize delamination during drilling of graphite-epoxy laminates
A neural network thrust force controller to minimize delamination during drilling of graphite-epoxy laminates
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DOI：
10.1016/0890-6955(96)00013-2
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发表时间：
1996-09-01
影响因子：
14
通讯作者：
Krishnamurthy, K
中科院分区：
文献类型：
--
作者：
Stone, R;Krishnamurthy, K
分层是一个公认的问题与钻孔纤维增强复合材料（FRCM）。最值得注意的问题发生在钻头进入和退出FRCM时。由于钻孔通常是装配期间的最后操作，因此通过钻孔过程在零件中引入的导致零件被拒收的任何缺陷都代表着昂贵的损失。基于线弹性断裂力学理论的研究已经提出了在各个钻孔区域中的临界切削力和推力，其可以用作防止裂纹生长或分层的指导。利用这些临界力曲线作为指导，推力控制器的开发，以尽量减少分层，而钻石墨-环氧树脂层压板。一个神经网络控制方案的实施，需要一个神经网络识别器来模拟钻削动力学和神经网络控制器来学习进给速度和所需的推力之间的关系。实验结果验证了这种控制方法的有效性以及鲁棒性的设计。分层区的视觉测量被用来量化推力控制钻孔过程与传统的恒定进给速率钻孔过程的好处。版权所有（C）1996 Elsevier Science Ltd
Delamination is a well-recognized problem associated with drilling fiber-reinforced composite materials (FRCMs). The most noted problems occur as the drill enters and exits the FRCM. Since drilling is often a final operation during assembly, any defects introduced in parts through the drilling process that result in the part being rejected represent an expensive loss. Studies based on linear-elastic fracture mechanics theory have proposed critical cutting and thrust forces in the various drilling regions that can be used as a guide in preventing crack growth or delamination. Using these critical force curves as a guide, a thrust force controller was developed to minimize the delamination while drilling a graphite-epoxy laminate. A neural network control scheme was implemented which required a neural network identifier to model the drilling dynamics and a neural network controller to learn the relationship between feed rate and the desired thrust force. Experimental results verifying the validity of this control approach as well as the robustness of the design are presented. Visual measurements of the delamination zones were used to quantify the benefits of the thrust force controlled drilling process versus the conventional constant feed rate drilling process. Copyright (C) 1996 Published by Elsevier Science Ltd